{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7c9e2048",
   "metadata": {},
   "source": [
    "# Fine-Tuning ChatGPT for Different Data Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e8ddc61",
   "metadata": {},
   "source": [
    "# Imports\n",
    "Here we import all the necessary packages and modules for our fine-tuning process.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b410a579",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e:\\Dropbox\\projects_active\\eth_uzh_hatespeech_llm_finetuning\\src\\gpt_finetuning\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "import pprint\n",
    "import time\n",
    "import argparse\n",
    "from ast import literal_eval\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from tqdm import tqdm\n",
    "\n",
    "import wandb\n",
    "from openai import OpenAI\n",
    "import pandas as pd\n",
    "\n",
    "from utils_2 import (\n",
    "    dataset_has_format_errors,\n",
    "    check_token_statistics_and_cost_estimate,\n",
    "    load_dataset_task_prompt_mappings,\n",
    "    write_jsonl,\n",
    ")\n",
    "from utils_1 import task_num_to_task_name, dataset_num_to_dataset_name, plot_count_and_normalized_confusion_matrix, task_to_display_labels\n",
    "\n",
    "# Get the notebook's full path\n",
    "notebook_path = os.getcwd()\n",
    "\n",
    "# Set module_dir to the notebook's directory\n",
    "module_dir = notebook_path\n",
    "\n",
    "#api_key_project = \"\" #MK's Key\n",
    "api_key_project = \"\" #new PRODIGI key\n",
    "\n",
    "client = OpenAI(\n",
    "    api_key=api_key_project,  # This is the default and can be omitted\n",
    ")\n",
    "\n",
    "\n",
    "WANDB_PROJECT_NAME = \"chatgpt_annotations_llm_comparison\"\n",
    "MODEL_NAME = 'gpt-4o-mini-2024-07-18'\n",
    "COMPLETION_RETRIES = 50\n",
    "\n",
    "print(notebook_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772bcc76",
   "metadata": {},
   "source": [
    "# Utility Functions\n",
    "These functions provide various utilities for tasks such as data loading, token statistics, and other preprocessing steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa7e1cb4",
   "metadata": {},
   "source": [
    "## Configuration Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c79142f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuration Variables\n",
    "# Manually set the variables that were previously handled by argparse\n",
    "\n",
    "# Type of task to run inference on\n",
    "task = 1  # Choices: [1]\n",
    "\n",
    "# Dataset to run inference on\n",
    "dataset = 5  # Choices: [1, 2, 3, 4, 5, 6]\n",
    "\n",
    "# Expert Dataset for Second Evaluation\n",
    "dataset_eval = 'x' # Choises: ['x']\n",
    "\n",
    "# Size of the sample to generate\n",
    "sample_size = '250'  # Choices: ['100','200']\n",
    "\n",
    "# Path to the directory to store the generated samples\n",
    "output_dir = '../../annotations/chatGPT/output_fine_tuning/'\n",
    "\n",
    "# Random seed to use\n",
    "seed = 2019\n",
    "\n",
    "# Path to the directory containing the datasets\n",
    "data_dir = '../../annotations/chatGPT/input_fine_tuning/'\n",
    "\n",
    "# Whether to use the full label\n",
    "not_use_full_labels = False\n",
    "\n",
    "# Path to the dataset-task mappings file\n",
    "dataset_task_mappings_fp = os.path.normpath(os.path.join(module_dir, '../../task_mappings', 'dataset_task_mappings.csv'))\n",
    "\n",
    "# Whether to rewrite the dataframe in OpenAI format\n",
    "rewrite_df_in_openai = True\n",
    "\n",
    "# Number of epochs to train the model\n",
    "n_epochs = 5\n",
    "\n",
    "# Batch Size for learning\n",
    "n_batch = 20\n",
    "\n",
    "# Name of the run\n",
    "run_name = 'finetune_chetGPT_hatespeech_prolific'\n",
    "\n",
    "# Temperature to use when generating text\n",
    "temp = 0.0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71e81ba8",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4ef6fa17",
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_label_to_completion(label: str, task_num: int, full_label: bool = True) -> str:\n",
    "    new_label = ''\n",
    "\n",
    "    if task_num == 1:\n",
    "        if full_label:\n",
    "            if str(label) in ['1.0', '1']:\n",
    "                new_label = 'HATE SPEECH'\n",
    "            elif str(label) in ['2.0', '2']:\n",
    "                new_label = 'TOXIC SPEECH'\n",
    "            else:\n",
    "                new_label = 'KEINE HATE SPEECH'\n",
    "            assert new_label in ['HATE SPEECH', 'TOXIC SPEECH', 'KEINE HATE SPEECH']\n",
    "        else:\n",
    "            if str(label) in ['1.0', '1']:\n",
    "                new_label = 'A'\n",
    "            elif str(label) in ['2.0', '2']:\n",
    "                new_label = 'C'\n",
    "            else:\n",
    "                new_label = 'B'\n",
    "            assert new_label in ['A', 'B', 'C']\n",
    "\n",
    "    return new_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "02978fd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_train_and_eval_sets(data_dir: str, dataset_num: int, task_num: int, sample_size: int, dataset_eval:str) \\\n",
    "        -> dict[str, pd.DataFrame]:\n",
    "    datasets = dict()\n",
    "\n",
    "    train_dataset_task_files = glob.glob(os.path.join(data_dir, f'ds_{dataset_num}__task_{task_num}_train_set*.csv'))\n",
    "    eval_set_name = f'ds_{dataset_num}__task_{task_num}_eval_set'\n",
    "    datasets[eval_set_name] = pd.read_csv(os.path.join(data_dir, eval_set_name + '.csv'), encoding='utf-8')\n",
    "\n",
    "    # Load the additional evaluation dataset specified by dataset_eval\n",
    "    second_eval_set_name = f'ds_{dataset_eval}__task_{task_num}_full_eval'\n",
    "    datasets[second_eval_set_name] = pd.read_csv(os.path.join(data_dir, second_eval_set_name + '.csv'), encoding='utf-8')\n",
    "\n",
    "    if sample_size == 'all':\n",
    "        train_dfs_ = {fn.strip('.csv'): pd.read_csv(fn, encoding='utf-8') for fn in train_dataset_task_files}\n",
    "        datasets.update(train_dfs_)\n",
    "    else:\n",
    "        train_df_fn = f'ds_{dataset_num}__task_{task_num}_train_set_{sample_size}'\n",
    "        datasets[train_df_fn] = pd.read_csv(os.path.join(data_dir, train_df_fn + '.csv'), encoding='utf-8')\n",
    "\n",
    "        if train_df_fn not in [os.path.basename(fn).strip('.csv') for fn in train_dataset_task_files]:\n",
    "            raise ValueError(f\"Sample size {sample_size} not found for\"\n",
    "                             f\" dataset {dataset_num} and task {task_num}\")\n",
    "\n",
    "    return datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "904e16c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_training_example(system_prompt, user_prompt_format, user_prompt_text, completion):\n",
    "    return {'messages': [\n",
    "        {'role': 'system',\n",
    "         'content': system_prompt},\n",
    "\n",
    "        {'role': 'user',\n",
    "         'content': user_prompt_format.format(text=user_prompt_text)},\n",
    "\n",
    "        {'role': 'assistant',\n",
    "         'content': completion}\n",
    "    ]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bdf692a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def upload_datasets_to_openai(output_dir, not_use_full_labels, rewrite_df_in_openai, datasets):\n",
    "    hatespeech_open_ai_metadata = list()\n",
    "\n",
    "    df_id_metadata = pd.DataFrame() if not os.path.exists('hatespeech_open_ai_metadata.csv') \\\n",
    "        else pd.read_csv('hatespeech_open_ai_metadata.csv')\n",
    "\n",
    "    for df_name, df in datasets.items():\n",
    "        df_jsonl_filename = os.path.join(output_dir, 'temp', df_name + '.jsonl')\n",
    "        write_jsonl(data_list=df['openai_instance_format'].tolist(), filename=df_jsonl_filename)\n",
    "\n",
    "        if not_use_full_labels:\n",
    "            df_name += '_single_letter_labels'\n",
    "\n",
    "        if (not rewrite_df_in_openai and\n",
    "                (len(df_id_metadata) > 0 and df_name in df_id_metadata['df_name'].tolist())):\n",
    "            print(f\"Dataset {df_name} already uploaded to OpenAI\")\n",
    "            continue\n",
    "\n",
    "        print(f\"Uploading {df_name} to OpenAI\")\n",
    "        df_response = client.files.create(\n",
    "            file=open(df_jsonl_filename, \"rb\"), purpose=\"fine-tune\"\n",
    "        )\n",
    "        df_response_dict = df_response.to_dict()\n",
    "        df_file_id = df_response_dict[\"id\"]\n",
    "\n",
    "        # Wait until the file is processed\n",
    "        while True:\n",
    "            file = client.files.retrieve(df_file_id)\n",
    "            file_dict = file.to_dict()\n",
    "            if file_dict[\"status\"] == \"processed\":\n",
    "                break\n",
    "            time.sleep(15)\n",
    "        hatespeech_open_ai_metadata.append({'df_name': df_name, 'file_id': df_file_id})\n",
    "\n",
    "    df_id_metadata = pd.concat([df_id_metadata, pd.DataFrame(hatespeech_open_ai_metadata)])\n",
    "    df_id_metadata.to_csv('hatespeech_open_ai_metadata.csv', index=False)\n",
    "\n",
    "    return df_id_metadata\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ace921b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fine_tune_chat_gpt(evaluation_file_id, training_file_id, model_name, n_epochs):\n",
    "    response = client.fine_tuning.jobs.create(\n",
    "        training_file=training_file_id,\n",
    "        validation_file=evaluation_file_id,\n",
    "        model=\"gpt-4o-mini-2024-07-18\",\n",
    "        suffix=model_name,\n",
    "        hyperparameters={\"n_epochs\": n_epochs,\n",
    "                         \"batch_size\": n_batch}\n",
    "    )\n",
    "\n",
    "    response_dict = response.to_dict()\n",
    "    job_id = response_dict[\"id\"]\n",
    "    print(\"Job ID:\", response_dict[\"id\"])\n",
    "    print(\"Status:\", response_dict[\"status\"])\n",
    "\n",
    "    # Wait until the job is done\n",
    "    while True:\n",
    "        job = client.fine_tuning.jobs.retrieve(job_id)\n",
    "        job_dict = job.to_dict()\n",
    "        if job_dict[\"status\"] == \"succeeded\":\n",
    "            break\n",
    "        elif job_dict[\"status\"] == \"failed\":\n",
    "            raise Exception(\"Training failed: %s\" % job_dict[\"error\"])\n",
    "        time.sleep(30)\n",
    "\n",
    "    return job_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2026f811",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_and_log_finetuning_event_history(job_id):\n",
    "    response = client.fine_tuning.jobs.list_events(fine_tuning_job_id=job_id)\n",
    "    response_dict = response.to_dict()\n",
    "    events = response_dict[\"data\"]\n",
    "    events.reverse()\n",
    "    for event in events:\n",
    "        print(event[\"message\"])\n",
    "\n",
    "    # Log events\n",
    "    for event in events:\n",
    "        if event['type'] != 'metrics':\n",
    "            continue\n",
    "        data = event['data']\n",
    "        wandb.log(\n",
    "            {\n",
    "                \"train_loss\": data.get(\"train_loss\"),\n",
    "                \"valid_loss\": data.get(\"valid_loss\"),\n",
    "                \"train_mean_token_accuracy\": data.get(\"train_mean_token_accuracy\"),\n",
    "                \"valid_mean_token_accuracy\": data.get(\"valid_mean_token_accuracy\")\n",
    "            },\n",
    "            step=data.get('step', 0)\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebc50a97",
   "metadata": {},
   "source": [
    "# Main Implementation\n",
    "This section contains the main code for fine-tuning the ChatGPT model. It includes setup, data preparation, training, and evaluation steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed1b7246",
   "metadata": {},
   "source": [
    "### Connect to WandB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "37564c84",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mkublima\u001b[0m (\u001b[33mdigdemlab\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "wandb version 0.19.7 is available!  To upgrade, please run:\n",
       " $ pip install wandb --upgrade"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.15.11"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "Run data is saved locally in <code>e:\\Dropbox\\projects_active\\eth_uzh_hatespeech_llm_finetuning\\src\\gpt_finetuning\\wandb\\run-20250228_140748-l9oes8e6</code>"
      ],
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       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Syncing run <strong><a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/l9oes8e6' target=\"_blank\">finetune_chetGPT_hatespeech_prolific</a></strong> to <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run at <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/l9oes8e6' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/l9oes8e6</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/l9oes8e6?jupyter=true' style='border:none;width:100%;height:420px;display:none;'></iframe>"
      ],
      "text/plain": [
       "<wandb.sdk.wandb_run.Run at 0x2b82fb877c0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 1/2\n",
    "# Initialize the Weights and Biases run\n",
    "wandb.init(\n",
    "    # set the wandb project where this run will be logged\n",
    "    project=WANDB_PROJECT_NAME,\n",
    "    name=run_name if run_name != '' else f'{MODEL_NAME}_ds_{dataset}_task_{int(task)}'\n",
    "                                                    f'_sample_{sample_size}_epochs_{n_epochs}'\n",
    "                                                    f'_full_label_names_{str(not not_use_full_labels)}'\n",
    "                                                    f'_temp_{temp}',\n",
    "\n",
    "    # track hyperparameters and run metadata\n",
    "    config = {\n",
    "        \"model\": MODEL_NAME,\n",
    "        \"dataset\": dataset_num_to_dataset_name[int(dataset)],\n",
    "        \"task\": task_num_to_task_name[int(task)],\n",
    "        \"epochs\": n_epochs,\n",
    "        \"temp\": temp\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58f1df25",
   "metadata": {},
   "source": [
    "### Load and Process Data Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2454b3ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 3\n",
    "# Load the dataset and filename\n",
    "dataset_idx, dataset_task_mappings = load_dataset_task_prompt_mappings(\n",
    "    dataset_num=dataset, task_num=task, dataset_task_mappings_fp=dataset_task_mappings_fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d35437fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 5\n",
    "# Load the train and eval datasets\n",
    "datasets = load_train_and_eval_sets(\n",
    "    data_dir=data_dir, dataset_num=dataset, task_num=task, sample_size=sample_size, dataset_eval=dataset_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9f379906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact prompts>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 4\n",
    "# Get information specific to the dataset\n",
    "label_column = dataset_task_mappings.loc[dataset_idx, \"label_column\"]\n",
    "system_prompt = dataset_task_mappings.loc[dataset_idx, 'zero_shot_prompt']\n",
    "user_prompt_format = dataset_task_mappings.loc[dataset_idx, 'user_prompt']\n",
    "    \n",
    "# Log the system prompt and user_prompt_format as files in wandb\n",
    "prompts_artifact = wandb.Artifact('prompts', type='prompts')\n",
    "with prompts_artifact.new_file('system_prompt.txt', mode='w', encoding='utf-8') as f:\n",
    "    f.write(system_prompt)\n",
    "with prompts_artifact.new_file('user_prompt_format.txt', mode='w', encoding='utf-8') as f:\n",
    "    f.write(user_prompt_format)\n",
    "wandb.run.log_artifact(prompts_artifact)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "47917349",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 6\n",
    "# Generate the training and evaluation examples in the way expected by the Open AI API to finetune chatgpt3.5\n",
    "preprocessed_output_dir = os.path.join(\n",
    "    output_dir, 'preprocessed', 'full_name_labels' if not not_use_full_labels else 'single_letter_labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5a9616d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 7\n",
    "for df_name, df in datasets.items():\n",
    "    df['completion_label'] = df[label_column].map(\n",
    "        lambda label: map_label_to_completion(label=label, task_num = task,\n",
    "                                              full_label=not not_use_full_labels)\n",
    "        )\n",
    "    df['openai_instance_format'] = df.apply(\n",
    "        lambda row: create_training_example(\n",
    "            system_prompt=system_prompt, user_prompt_format=user_prompt_format,\n",
    "            user_prompt_text=row['text'],\n",
    "            completion=row['completion_label']\n",
    "        ),\n",
    "        axis=1\n",
    "    )\n",
    "    df['openai_instance_without_completion'] = df['openai_instance_format'].map(lambda x: x['messages'][:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8bcd8c12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Check for errors ds_5__task_1_train_set_250 set: \n",
      "No errors found\n"
     ]
    }
   ],
   "source": [
    "# Section 8\n",
    "print(f'Check for errors {df_name} set: ')\n",
    "assert not dataset_has_format_errors(df['openai_instance_format'].tolist()), f\"Errors found in {df_name}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9b1d7833",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 9\n",
    "df.to_csv(os.path.join(preprocessed_output_dir, df_name + '.csv'), index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24cdc6b4",
   "metadata": {},
   "source": [
    "### Upload Training Dataset to OPEN AI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2fd9b187",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploading ds_5__task_1_eval_set to OpenAI\n",
      "Uploading ds_x__task_1_full_eval to OpenAI\n",
      "Uploading ds_5__task_1_train_set_250 to OpenAI\n"
     ]
    }
   ],
   "source": [
    "# Section 10\n",
    "# Create jsonl file and upload to OpenAI\n",
    "df_id_metadata =upload_datasets_to_openai(output_dir, not_use_full_labels, rewrite_df_in_openai, datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "705758da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 11\n",
    "# delete all files in the temp folder\n",
    "os.system(f\"rm -rf {os.path.join(output_dir, 'temp')}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4519ab5a",
   "metadata": {},
   "source": [
    "### Finetune chatGPT with the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b0ec6e82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 12\n",
    "# Run training on the train_df samples selected\n",
    "eval_set_name = f'ds_{dataset}__task_{task}_eval_set'\n",
    "eval_df = datasets[eval_set_name]\n",
    "for df_name, df in datasets.items():\n",
    "    if df_name == eval_set_name:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a76b1ab7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finetuning ds_5__task_1_train_set_250\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Section 13\n",
    "print(f\"Finetuning {df_name}\")\n",
    "print('-' * 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a1036bc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 14\n",
    "# Log in wandb.config the dataset sample size used\n",
    "sample_size = df_name.split('_')[-1]\n",
    "wandb.config['trainset_size'] = sample_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d0860e65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for the evaluation set: \n",
      "\n",
      "#### Distribution of num_messages_per_example:\n",
      "min / max: 3, 3\n",
      "mean / median: 3.0, 3.0\n",
      "p5 / p95: 3.0, 3.0\n",
      "\n",
      "#### Distribution of num_total_tokens_per_example:\n",
      "min / max: 847, 999\n",
      "mean / median: 879.7842639593908, 869.0\n",
      "p5 / p95: 853.0, 920.0\n",
      "\n",
      "#### Distribution of num_assistant_tokens_per_example:\n",
      "min / max: 4, 6\n",
      "mean / median: 5.187817258883249, 6.0\n",
      "p5 / p95: 4.0, 6.0\n",
      "\n",
      "0 examples may be over the 4096 token limit, they will be truncated during fine-tuning\n",
      "Dataset has ~346635 tokens that will be charged for during training\n",
      "By default, you'll train for 5 epochs on this dataset\n",
      "By default, you'll be charged for ~1733175 tokens\n"
     ]
    }
   ],
   "source": [
    "# Section 15\n",
    "# Check statistics and possible cost for each dataset\n",
    "print('Statistics for the evaluation set: ')\n",
    "eval_tokens = check_token_statistics_and_cost_estimate(\n",
    "    datasets[eval_set_name]['openai_instance_format'].tolist(), target_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "254bdfe1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for the training set: \n",
      "\n",
      "#### Distribution of num_messages_per_example:\n",
      "min / max: 3, 3\n",
      "mean / median: 3.0, 3.0\n",
      "p5 / p95: 3.0, 3.0\n",
      "\n",
      "#### Distribution of num_total_tokens_per_example:\n",
      "min / max: 847, 997\n",
      "mean / median: 880.812, 868.5\n",
      "p5 / p95: 853.0, 928.2\n",
      "\n",
      "#### Distribution of num_assistant_tokens_per_example:\n",
      "min / max: 4, 6\n",
      "mean / median: 4.932, 4.0\n",
      "p5 / p95: 4.0, 6.0\n",
      "\n",
      "0 examples may be over the 4096 token limit, they will be truncated during fine-tuning\n",
      "Dataset has ~220203 tokens that will be charged for during training\n",
      "By default, you'll train for 5 epochs on this dataset\n",
      "By default, you'll be charged for ~1101015 tokens\n"
     ]
    }
   ],
   "source": [
    "# Section 16\n",
    "print(f'Statistics for the training set: ')\n",
    "train_tokens = check_token_statistics_and_cost_estimate(df['openai_instance_format'].tolist(),\n",
    "                                                        target_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "91aed7d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of tokens: 566838\n",
      "\n",
      "#### Estimated cost: 4.53 USD\n"
     ]
    }
   ],
   "source": [
    "# Section 17\n",
    "print(f\"Total number of tokens: {eval_tokens + train_tokens}\\n\")\n",
    "print(f\"#### Estimated cost: {round((eval_tokens + train_tokens) * (0.008 / 1000), 2)} USD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5a4cb776",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 18\n",
    "# Run the fine-tuning job\n",
    "training_file_id = df_id_metadata.loc[df_id_metadata['df_name'] == df_name, 'file_id'].values[0]\n",
    "evaluation_file_id = df_id_metadata.loc[df_id_metadata['df_name'] == eval_set_name, 'file_id'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6b0baf56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job ID: ftjob-wATI3cd5H6O451bDNXmwWhjl\n",
      "Status: validating_files\n"
     ]
    }
   ],
   "source": [
    "# Section 19\n",
    "model_name = (df_name.replace('__', '_')\n",
    "              .replace('train_set', 'trn')\n",
    "              .replace('task', 't')\n",
    "              .replace('_single_letter_labels', '_sl'))\n",
    "job_id = fine_tune_chat_gpt(evaluation_file_id, training_file_id, model_name=model_name, n_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6aff8caf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model ft:gpt-4o-mini-2024-07-18:university-of-zurich:ds-5-t-1-trn-250:B5uaxLGl has been successfully fine-tuned\n"
     ]
    }
   ],
   "source": [
    "# Section 20\n",
    "# Print the model name\n",
    "response = client.fine_tuning.jobs.retrieve(job_id)\n",
    "response_dict = response.to_dict()\n",
    "full_model_name = response_dict[\"fine_tuned_model\"]\n",
    "print(f'The model {full_model_name} has been successfully fine-tuned')\n",
    "wandb.config['model_name_openai'] = full_model_name\n",
    "wandb.config['finetuning_jobid'] = job_id\n",
    "wandb.config['training_file_openai_id'] = response_dict['training_file']\n",
    "wandb.config['validation_file_openai_id'] = response_dict['validation_file']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "45b169bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 48/63: training loss=0.01, validation loss=0.01\n",
      "Step 49/63: training loss=0.01, validation loss=0.18\n",
      "Step 50/63: training loss=0.02, validation loss=0.14\n",
      "Step 51/63: training loss=0.00, validation loss=0.04\n",
      "Step 52/63: training loss=0.00, validation loss=0.14, full validation loss=0.08\n",
      "Step 53/63: training loss=0.00, validation loss=0.03\n",
      "Step 54/63: training loss=0.00, validation loss=0.08\n",
      "Step 55/63: training loss=0.00, validation loss=0.10\n",
      "Step 56/63: training loss=0.01, validation loss=0.13\n",
      "Step 57/63: training loss=0.04, validation loss=0.05\n",
      "Step 58/63: training loss=0.00, validation loss=0.01\n",
      "Step 59/63: training loss=0.02, validation loss=0.09\n",
      "Step 60/63: training loss=0.01, validation loss=0.02\n",
      "Step 61/63: training loss=0.03, validation loss=0.03\n",
      "Step 62/63: training loss=0.00, validation loss=0.07\n",
      "Step 63/63: training loss=0.11, validation loss=0.04, full validation loss=0.09\n",
      "Checkpoint created at step 39\n",
      "Checkpoint created at step 52\n",
      "New fine-tuned model created\n",
      "The job has successfully completed\n"
     ]
    }
   ],
   "source": [
    "# Section 21\n",
    "# Print the events (training history of the model)\n",
    "print_and_log_finetuning_event_history(job_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c579b37",
   "metadata": {},
   "source": [
    "## Evaluate Model on Validation Set with Annotations of the Group from which the Training Data is!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fba537b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##################################################\n",
      "Getting predictions on the evaluation set\n"
     ]
    }
   ],
   "source": [
    "# Section 22\n",
    "# Evaluate the model on the evaluation set and store the predictions\n",
    "print(\"\\n\" + \"#\" * 50)\n",
    "print(\"Getting predictions on the evaluation set\")\n",
    "predictions = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0a544a5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/394 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 394/394 [03:40<00:00,  1.79it/s]\n"
     ]
    }
   ],
   "source": [
    "# Section 23\n",
    "for messages in tqdm(eval_df['openai_instance_without_completion'].tolist()):\n",
    "    # Retry the completion at least COMPLETION_RETRIES times\n",
    "    num_retries = 2\n",
    "    response = None\n",
    "    while num_retries < COMPLETION_RETRIES and response is None:\n",
    "        try:\n",
    "            response = client.chat.completions.create(\n",
    "                model=full_model_name,\n",
    "                messages=messages,\n",
    "                temperature=temp,\n",
    "                n=1\n",
    "            )\n",
    "        except Exception as e:\n",
    "            print('Error getting predictions. Retrying...')\n",
    "            time.sleep(5)\n",
    "            num_retries += 1\n",
    "            if num_retries >= COMPLETION_RETRIES:\n",
    "                print('Maximum amount of retires reached')\n",
    "                raise e\n",
    "    response_dict = response.to_dict()\n",
    "    predictions.append(response_dict['choices'][0]['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "337b13ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 24\n",
    "# Add predictions to df\n",
    "eval_df['prediction'] = predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fe242636",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 25\n",
    "# Store output\n",
    "predictions_output_dir = os.path.join(output_dir, 'predictions',\n",
    "                                      f'dataset_{dataset}_task_{task}')\n",
    "os.makedirs(predictions_output_dir, exist_ok=True)\n",
    "datasets[eval_set_name].to_csv(\n",
    "    os.path.join(predictions_output_dir, f\"{model_name}-{run_name}.csv\"),\n",
    "    index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bcaddde5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 26\n",
    "# Get performance metrics\n",
    "y_true = eval_df['completion_label']\n",
    "y_pred = eval_df['prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3d085eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 27\n",
    "label_type = 'full_name' if not not_use_full_labels else 'short_name'\n",
    "display_labels = task_to_display_labels[task][label_type]\n",
    "labels = display_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a1ce1492",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\maelk\\miniconda3\\envs\\chatGPT\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\maelk\\miniconda3\\envs\\chatGPT\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\maelk\\miniconda3\\envs\\chatGPT\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HATE SPEECH': {'f1-score': 0.8380952380952381,\n",
      "                 'precision': 0.8407643312101911,\n",
      "                 'recall': 0.8354430379746836,\n",
      "                 'support': 158},\n",
      " 'KEINE HATE SPEECH': {'f1-score': 0.8912579957356076,\n",
      "                       'precision': 0.8818565400843882,\n",
      "                       'recall': 0.9008620689655172,\n",
      "                       'support': 232},\n",
      " 'TOXIC SPEECH': {'f1-score': 0.0,\n",
      "                  'precision': 0.0,\n",
      "                  'recall': 0.0,\n",
      "                  'support': 4},\n",
      " 'accuracy': 0.8654822335025381,\n",
      " 'macro avg': {'f1-score': 0.5764510779436153,\n",
      "               'precision': 0.574206957098193,\n",
      "               'recall': 0.578768368980067,\n",
      "               'support': 394},\n",
      " 'weighted avg': {'f1-score': 0.8608906158114431,\n",
      "                  'precision': 0.8564250802811885,\n",
      "                  'recall': 0.8654822335025381,\n",
      "                  'support': 394}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1DPdy+XFN1cOfnGqwtbVF06ZNVcoyMzNVXp8/fx5Hjx7F8ePHMWPGDGW5XC7Hxo0b8frrr2u9Pz8/P50CQwBo0qQJmjRpgoiICMTExCAgIADJycl49dVXAZTPv/v9999hZmYGDw8P2NraqrXh6uqq9j4f5e3tXWmdgIAAXLhwQav+anNMH2VlZQUrKyut2q8PLMQCGrYpxOWDUrTtf09ZfumgFK373tO4TckDM5g/8tktMiv/6i8IgHuTB5iy57TK+t2LvFFcaI7Bc9Lh6MV5knWhTG6OS1dc8HS7LPx23EdZ/nS7mzh0vPpfVs5ddEfP0CsQiQQI/04Ub+Qlw5271gzy6oGyUjNc/sMGT3crwKHdUmX5090KcHiPtIotSVdyiCDX84bH+m5fW+pH3rEeSkxMRLdu3XD69GmcOnVKuUyfPr1aw7f68PX1hY2NDQoL/5vMa2ZmhqZNm8Lf319jkGcII0aMwM8//4yTJ0+qrSsrK1PpD5XrFpGFY8nuOPaNG279JcH2WB/k3bRC8MhbAIBdC7yxcXITZf1WvfNwZo8TDn3lgTvXrXA1zQ7b3veFd/t/IPUohaVEgGfzByqLxEEOK1s5PJs/gIWY40F1ZcvOVhjQ+zL69byMxg3zEPnKMbi7FmJnSnn29bXhJzB90gGVbZr43EUTn7uwlpRB6lCEJj530bhhnnL9jp+aw8G+GBPDj6GhVz46dcjE8P+dwfY92k2hoLq39QtX9B9xF2HD7sC7aREmzL0B94al+GGdS113zahw6Jb0Ulpaiq+++gqxsbFqF0RERERg4cKFOH36NNq3125ydE5ODoqKilTKXFxcNF7MMHfuXNy/fx/PPvssfHx8kJeXh2XLlqG0tBR9+/bV6X0UFBSo3a/PxsZG5VYvd+7cUavj6OgIiUSC6Oho/PDDD+jduzfmzZuHrl27wt7eHmlpaViwYAESExN5e5VHPDXwDu7nWeDnTxpBlmsJz4D7GLvmTzg1Ks+8yXLEyLvxXxaz45BcFBea4dA6D+z8oDGsHeRoEpKP5965XldvgbS077AfHOyLMerF03B2eoD0DEfExPdW3g7FxekB3F1Vvwyt+GiH8v8BTe6g9zNXkZ1ji9FvvAQAyL1ji3fm98XrrxzHFx9tx+27Nvjux5ZqV/fSk2vfdifYO8kx8u1bcHYvw7WLErw7yg85ldxLk+hxGOjVgO3bt+POnTv43//+p7auWbNmaNu2LRITE7Fs2TKt2tN08cXhw4fRpUsXtfLu3bvj888/x5gxY3Dr1i04OTmhQ4cO+Omnn6q8iEOT2bNnY/bs2SplEyZMwIoVK5Sv+/Tpo7bdxo0bMWzYMFhZWSElJQVLlizBypUrMXXqVNjY2KBly5aIiorS6apgUxIy+hZCRt/SuG7Yx3+rlXUNv4Wu4Zrra9sG1Y0dP7XAjp80Z9s+Wt5Vrazvy688ts0Ll90R9e5zeveN6s7Ota7Yuda1rrth1OTQf+hVbpiu1DiRIOhzjRfRk0Mmk0EqleLA2Qaws68fKXWqvimjIuu6C1SLzA6equsuUC0oE0qRim3Iz89Xe1CAIVR8Trx7JAwSO/1u8VX0Tynmd/mpxvpqKMzoERERkUmRC2aQ6znHTt/ta0v96CURERER6YwZPSIiIjIpAkRQ6DlHT6gnt1dhoEdEREQmhUO3RERERFTvMaNHREREJkUhiKAQ9Bt61Xf72sJAj4iIiEyKHGaQ6zmoqe/2taV+9JKIiIiIdMaMHhEREZkUDt0SERERGSkFzKDQc1BT3+1rS/3oJRERERHpjBk9IiIiMilyQQS5nkOv+m5fWxjoERERkUnhHD0iIiIiIyUIZlDo+WQLgU/GICIiIqK6xIweERERmRQ5RJBDzzl6em5fWxjoERERkUlRCPrPsVMIBupMDePQLREREZGRYkaPiIiITIrCABdj6Lt9bWGgR0RERCZFAREUes6x03f72lI/wlEiIiIi0hkzekRERGRS+GQMIiIiIiNlSnP06kcviYiIiEhnzOgRERGRSVHAAM+6rScXYzDQIyIiIpMiGOCqW4GBHhEREdGTRyEYIKNXTy7G4Bw9IiIiIiPFjB4RERGZFFO66paBHhEREZkUDt0SERERUb3HjB4RERGZFFN61i0DPSIiIjIpHLolIiIionqPGT0iIiIyKaaU0WOgR0RERCbFlAI9Dt0SERERGSlm9IiIiMikmFJGj4EeERERmRQB+t8eRTBMV2ocAz0iIiIyKaaU0eMcPSIiIiIjxYweERERmRRTyugx0CMiIiKTYkqBHoduiYiIiIwUM3pERERkUkwpo8dAj4iIiEyKIIgg6Bmo6bt9beHQLREREZGRYkaPiIiITIoCIr1vmKzv9rWFgR4RERGZFFOao8ehWyIiIiIjxYweERERmRRejEFERERkpCqGbvVdqmP58uXw8/ODRCJBYGAgDhw4UGX94uJixMTEwMfHB1ZWVmjSpAlWr16t9f6Y0SMiIiKTUlcZveTkZERHR2P58uUIDQ3FypUrMWDAAJw/fx6NGzfWuM3LL7+MW7duITExEU2bNkVOTg7Kysq03icDPSIiIqJasHjxYowdOxYREREAgKVLl2LPnj1ISEhAfHy8Wv3du3dj3759uHLlCpydnQEAvr6+Ou2TgR4ZnbkvvAwLc6u67gbVsJTUpLruAtWifg2equsukBERDHDVbUVGTyaTqZRbWVnBykr9M6ikpAQnTpzAO++8o1IeFhaGQ4cOadzH9u3bERQUhIULF+Krr76Cra0tBg0ahHnz5sHa2lqrfjLQIyIiIpMiABAE/dsAAG9vb5XyOXPmYO7cuWr1b9++DblcDg8PD5VyDw8PZGdna9zHlStXcPDgQUgkEnz33Xe4ffs2Jk6ciLt372o9T4+BHhEREVE1ZWRkwMHBQflaUzbvYSKRaiZREAS1sgoKhQIikQjr16+HVCoFUD78+9JLL+Hzzz/XKqvHQI+IiIhMigIiiAz0ZAwHBweVQK8yrq6uMDc3V8ve5eTkqGX5Knh5eaFhw4bKIA8AWrZsCUEQkJmZiWbNmj12v7y9ChEREZmUiqtu9V10IRaLERgYiJSUFJXylJQUhISEaNwmNDQUN2/exD///KMsu3TpEszMzNCoUSOt9stAj4iIiKgWTJ48GatWrcLq1atx4cIFvP3227h+/ToiIyMBADNnzsSYMWOU9UeMGAEXFxe8+uqrOH/+PPbv349p06bhtdde48UYRERERJooBBFEdfCs26FDh+LOnTuIjY1FVlYW2rRpg127dsHHxwcAkJWVhevXryvr29nZISUlBW+++SaCgoLg4uKCl19+GfPnz9d6nwz0iIiIyKQIggGuuq3m9hMnTsTEiRM1rktKSlIra9Gihdpwry44dEtERERkpJjRIyIiIpNSV49AqwsM9IiIiMikMNAjIiIiMlJ1dTFGXeAcPSIiIiIjxYweERERmZS6vOq2tjHQIyIiIpNSHujpO0fPQJ2pYRy6JSIiIjJSzOgRERGRSeFVt0RERERGSvh30beN+oBDt0RERERGihk9IiIiMikcuiUiIiIyViY0dstAj4iIiEyLATJ6qCcZPc7RIyIiIjJSzOgRERGRSeGTMYiIiIiMlCldjMGhWyIiIiIjxYweERERmRZBpP/FFPUko8dAj4iIiEyKKc3R49AtERERkZFiRo+IiIhMC2+YTERERGScTOmqW60CvWXLlmndYFRUVLU7Q0RERESGo1Wgt2TJEq0aE4lEDPSIiIjoyVdPhl71pVWgd/Xq1ZruBxEREVGtMKWh22pfdVtSUoKLFy+irKzMkP0hIiIiqlmCgZZ6QOdA7/79+xg7dixsbGzQunVrXL9+HUD53LwPP/zQ4B0kIiIiourROdCbOXMmTp8+jdTUVEgkEmV5nz59kJycbNDOERERERmeyEDLk0/n26t8//33SE5ORpcuXSAS/fcmW7Vqhb///tugnSMiIiIyOBO6j57OGb3c3Fy4u7urlRcWFqoEfkRERERUt3QO9Dp27IgffvhB+boiuPvyyy8RHBxsuJ4RERER1QQTuhhD56Hb+Ph49O/fH+fPn0dZWRk++eQTnDt3DocPH8a+fftqoo9EREREhiOIyhd926gHdM7ohYSE4LfffsP9+/fRpEkT/PTTT/Dw8MDhw4cRGBhYE30kIiIiomqo1rNu27Zti7Vr1xq6L0REREQ1ThDKF33bqA+qFejJ5XJ89913uHDhAkQiEVq2bInBgwfDwqJazRERERHVHhO66lbnyOzs2bMYPHgwsrOz0bx5cwDApUuX4Obmhu3bt6Nt27YG7yQRERER6U7nOXoRERFo3bo1MjMz8fvvv+P3339HRkYG2rVrh/Hjx9dEH4mIiIgMp+JiDH2XekDnjN7p06eRlpYGJycnZZmTkxM++OADdOzY0aCdIyIiIjI0kVC+6NtGfaBzRq958+a4deuWWnlOTg6aNm1qkE4RERER1RgTuo+eVoGeTCZTLnFxcYiKisLmzZuRmZmJzMxMbN68GdHR0ViwYEFN95eIiIiItKTV0K2jo6PK480EQcDLL7+sLBP+vcZ44MCBkMvlNdBNIiIiIgMxoRsmaxXo7d27t6b7QURERFQ7eHsVVd27d6/pfhARERGRgVX7Dsf379/H9evXUVJSolLerl07vTtFREREVGOY0atcbm4uXn31Vfz4448a13OOHhERET3RTCjQ0/n2KtHR0bh37x6OHDkCa2tr7N69G2vXrkWzZs2wffv2mugjEREREVWDzhm9X3/9Fdu2bUPHjh1hZmYGHx8f9O3bFw4ODoiPj8dzzz1XE/0kIiIiMgwTuupW54xeYWEh3N3dAQDOzs7Izc0FALRt2xa///67YXtHREREZGAVT8bQd6kPdM7oNW/eHBcvXoSvry+eeuoprFy5Er6+vlixYgW8vLxqoo9ERu25wX/jxWGX4exShGtXHfDFZ+1w7oxrpfXbtM/FuIln4OMnw53bEmzZFIBd2/2V683NFXh55EX06XcdLm4PkHndHmu+aI0TxzyVdaytSzF67HmEdL0JqVMx/r7siJWftsPli841+l7p8XYkueDbBHfczbGET0ARImNvoG3nwkrrb1/jiu1rXHErUwz3BiUY9tYt9B1yrxZ7TIb2/Cu3MeT1XDi7l+LaJQlWzG6As8fs6rpbVE9Va45eVlYWAGDOnDnYvXs3GjdujGXLliEuLk6ntsLDw/HCCy+olG3evBkSiQQLFy4EAMydOxcikUhtadGihXKbHj16IDo6WuW1SCTCpk2bVNpeunQpfH19la+TkpI0ti2RSCrtc2pqKkQiEfLy8tTW+fr6YunSpWrlcXFxMDc3x4cffqhSV9O+K5YePXpUWe/hth515coVDB8+HA0aNIBEIkGjRo0wePBgXLp0SVnn4bbs7e0RFBSErVu3Ktdre9w11YmMjFTpz969e/Hss8/CxcUFNjY2aNWqFaZMmYIbN25U+5gai249MzH+jT+Q/HVzvBnRC+fOuCB24W9wc7+vsb6HZyFiPzyEc2dc8GZEL3yzvjkmvHkaod1uKOuMGXseAwZeRcKy9oh8pS92bffDu/OOwL9pnrLOW9N+R4fAHCyK64iJr/XByTR3xH18EC6uD2r6LVMVUrc5YsWchhgedQvLf7qINp0L8e5If+RkWmqsv2OtC9bEe2HUlGx8sfdPjJ6ajc9nNcKRnxxquedkKN0H3UPk+zexcZk7JoYF4OxRW8xffxVuDUsevzFpj49Aq9zIkSMRHh4OAOjQoQPS09Nx/PhxZGRkYOjQoXp1ZtWqVRg5ciQ+++wzTJ8+XVneunVrZGVlqSwHDx6ssi2JRIJ3330XpaWlVdZzcHBQa/vatWt6vY9HrVmzBtOnT8fq1auVZcePH1fub8uWLQCAixcvKsseDrpiY2PV+vjmm29q3FdJSQn69u0LmUyGrVu34uLFi0hOTkabNm2Qn5+v1q+srCwcP34c7du3x5AhQ3D48GHlem2O+7hx49TqVATpALBy5Ur06dMHnp6e2LJlC86fP48VK1YgPz8fH3/8cfUPqpH435DL+GmXL/b84IeM6w744rP2yM2xwXODr2is/+ygq8jJscEXn7VHxnUH7PnBDyk/+uL/hl5W1ukVdh3frG+OtKOeyM6yxa7t/vj9uIeyjlgsR2j3m1i9sg3O/uGKrBt2WJ/UCtnZtpXul2rH1i/c0G/4XQwYeReNmxXj9dgbcGtQip3rNGd4f9nsjGdH3UGPwXnw8ilBjxfy0G/4XXzzuXst95wM5f/G38aejc7YvcEFGX9JsGJOQ+TetMTzY+7Uddeonqr2ffQq2NjY4Omnn9a7IwsXLsTs2bOxYcMGvPjiiyrrLCws4OnpWcmWmg0fPhw7duzAl19+iYkTJ1ZaTyQS6dy2Lvbt24cHDx4gNjYW69atw/79+9GtWze4ubkp6zg7lw+Xubu7w9HRUa0Ne3t7rft4/vx5XLlyBb/++it8fHwAAD4+PggNDVWr6+joCE9PT3h6emLFihXYtGkTtm/fjuDgYADaHXcbG5tK62RmZiIqKgpRUVFYsmSJstzX1xfdunXTmMEzJRYWCjRtnodvNgSolJ887o6Wre9q3KZl6zs4eVz1Q/zEMXeEPZsOc3MF5HIzWFoqUFJirlKnuNgcrduWf1CYmytgbi6o1SkpNkertvwwqSulJSJc/sMGQ9/IUSkP7F6A82m2lW4jlihUyqwkClw8ZYOyUsBCcyKQnlAWlgo0a3cfyZ898ju+zx6tgiofvifdiaD/HLv6cSmGloHe5MmTtW5w8eLFOnfinXfeweeff46dO3eiT58+Om+viYODA2bNmoXY2Fi88sorsLXV/IeypiUmJmL48OGwtLTE8OHDkZiYiG7dutXY/tzc3GBmZobNmzcjOjoa5ubmj98IgKWlJSwsLB6bAdXFt99+i5KSEpXs7MM0BbW6KC4uRnFxsfK1TCbTq73a5iAthrm5gLx7qlMF7t2zgpNzkcZtnJyLce+elUpZ3j0JLCwEOEiLce+uNX4/7o7/DbmMs6ddkXXTFk89nYMuoVkwNyv/q/bggSXOn3XG8DF/IuOaPfLuSdC9dwaat7yLm5mcB1RXZHfNoZCL4Oiq+jvo6FaKezn2GrcJ7FGA3RtcENI/H03bPsDlP6yxZ5MzykrNkH/XAi4eZbXRdTIQB2c5zC2AvNuqH815uRZwcue5pOrRauj25MmTWi2nTp3SuQM//vgjFixYgG3btlUa5J05cwZ2dnYqS0RExGPbnjhxIiQSSZXBZ35+vlrbYWFhj227UaNGattdv35dpY5MJsOWLVswatQoAMCoUaOwefNmnQOSGTNmqO0rNTVVY92GDRti2bJlmD17NpycnNCrVy/MmzcPV65UPiRXXFyM+fPnQyaToXfv3spybY778uXL1eqsXbsWAHD58mU4ODhofZGONsf0YfHx8ZBKpcrF29tbq/08aYRHvlWKRI+Z+vHIJf0i5cvy/6z4tD1u3rDDynU/YfvP3+P1t07j5x99IFf8t92iuCCIAHy95UdsS/keg/7vb6T+4g2For58RzVeokdOgSCIKk0djIzORlBPGd56PgDPNm6Pua/6oe/L5dlgLb/j0RNI09+E+jIfrN6ouL2Kvks9oFVGb+/evTXWgXbt2uH27duYPXs2OnbsCHt79W+uzZs3V7sZs6Z6j7KyskJsbCzeeOMNvP766xrr2Nvbq90Wxtra+rFtHzhwQK0PFRdQVNiwYQP8/f3Rvn17AMBTTz0Ff39/bNq0CePHj3/sPipMmzZNOS+yQsOGDSutP2nSJIwZMwZ79+7F0aNH8e233yIuLg7bt29H3759lfWGDx8Oc3NzPHjwAFKpFIsWLcKAAQOU67U57iNHjkRMTIxKWcXtdwRBgOjRT60qaHNMHzZz5kyVbLNMJqtXwZ4s3wpyuUgte+foWIy8u5ovCLp3Vz3bJ3UsQlmZCLJ8sbLdee8Gw1Ish4NDCe7cluDV8edwK8tGuU32TTvMiO4GK0kZbGxKce+uNd6ZfRTZD9Wh2uXgLIeZuYB7uarjrfm3LeDkpjmbY2UtYMqSDLy1MAP3ci3h7FGKXV+7wMZODgdnZoDqG9ldc8jLoHa+pa5luJer90wrepgJPRmjzn9yGjZsiC1btqBnz57o378/du/erfZhLxaL0bRp02q1P2rUKCxatAjz589XueK2gpmZWbXa9vPzUxt6tLBQPZyrV6/GuXPnVMoVCgUSExN1CvRcXV117qO9vT0GDRqEQYMGYf78+ejXrx/mz5+vEugtWbIEffr0gYODgzI4e5g2x10qlVZaJyAgAPn5+cjKytIqq6fNMX2YlZUVrKysKl3/pCsrM8NfFx3RISgHhw/+F7h3CMrBkd80H68L51zQOSRLpezpjjm4fNEJcrlqgr60xBx3blvD3FyB0O43cGCv+peD4iILFBdZwM6uBE93ysHqFW0M8M6oOizFApq1u4/f99sjdMB/F079vt8ewf3yq9iyfC6eW4PyId9925zQqY8MZjpfakd1razUDJf/sMHT3QpwaLdUWf50twIc3iOtYkuiyj0RfwoaN26Mffv2IScnB2FhYQada2VmZoa4uDgkJCQgPT3dYO0+zpkzZ5CWlobU1FScOnVKuezfvx/Hjx/H2bNna60vFbdFKSxUnczr6emJpk2bagzyDOGll16CWCxWuQr3YaZ+MQYAfPdtM/R7Lh19B6TDu7EM4yb9ATeP+8r74oWPO4spM9OU9Xdt94O7x32Mm/gHvBvL0HdAOsKeTcfW5GbKOs1b3kXIMzfg6VWI1m1vY97C3yASCdi86b+LPp7ueAuBnbLh4VmIDoG3EL/0AG5ct0PKjz619+ZJzf+Nz8XuDc7Ys9EZ1y9bYcWcBsi5YYnnxtwGAKyO88LCqMbK+pl/W+GXLU64cUWMP0/aIC7SB+kXJXh1ZlZlu6An3NYvXNF/xF2EDbsD76ZFmDD3BtwbluKHdS513TXjYkK3V6nzjF6FRo0aITU1FT179kRYWBj27NkDqbT8G0xZWRmys7NV6otEInh4eGjV9vPPP4/OnTtj5cqVatsIgqDWNlA+/Gimx1fixMREdOrUSeOFF8HBwUhMTFS5ErUqBQUFan20sbGBg4P6vbJOnTqFOXPmYPTo0WjVqhXEYjH27duH1atXY8aMGTq9B22O+/3799XqWFlZwcnJCd7e3liyZAneeOMNyGQyjBkzBr6+vsjMzMS6detgZ2dn8rdY2b+3EewdijHilT/h7FyE9KsOmDMjFDm3yodQnVyK4Obx3z31bmXbYvY7IRg/6Q88/8IV3LkjwcpP2+O3/f9l6yzFcowZex6eDQrx4IEF0o54YlFcEAr/ESvr2NqWInzcObi6PUBBgSV+298Qa1e1VssKUu3qMTgPBffMsX6JJ+7mWMCneRHmf30FHo3Ks3V3cyyRe+O/86hQAFtWuCHzb2+YWwpoH/IPlmy7DE9v3nOtvtq33Qn2TnKMfPsWnN3LcO2iBO+O8kPOQ+ed9GeIJ1sY7ZMxalLDhg2xb98+9OzZE3379sVPP/0EADh37pza0J+VlRWKijRfmajJggULEBISolYuk8k0DitmZWVV+7YrJSUl+PrrrysNrF588UXEx8djwYIFEIsf/8s7e/ZszJ49W6VswoQJWLFihVrdRo0awdfXF++//z7S09MhEomUr99++22d3oc2x/3LL7/El19+qVKnX79+2L17N4DyC2ICAgKwaNEi/O9//8ODBw/g6+uL559/XqeruY3ZD9ua4IdtTTSuW/JhkFrZ2dNuiBrfW0Pt/9ZHhvetdD0AHEhthAOpjXTrKNWKgeF3MDBc821upi5VvTipcbNiLE+5pLEu1V8717pi59rKn45DpAuRIDx6fQ9R/SSTySCVStG7aTQszOvv3D3Szq7ULXXdBapF/Ro8VdddoFpQJpQiFduQn5+vcdRKXxWfE77zP4BZFU/B0oaiqAjp78bUWF8NpVrjNF999RVCQ0PRoEED5VMkli5dim3bthm0c0REREQGZ0Jz9HQO9BISEjB58mQ8++yzyMvLg1wuB1B+81tjfiYpERERUX2jc6D36aef4ssvv0RMTIzKUxeCgoJw5swZg3aOiIiIyNAqLsbQd6kPdL4Y4+rVq+jQoYNauZWVldrtO4iIiIieOIZ4skU9eTKGzhk9Pz8/jY86+/HHH9GqVStD9ImIiIio5pjQHD2dM3rTpk3DpEmTUFRUBEEQcOzYMWzcuBHx8fFYtWpVTfSRiIiIiKpB50Dv1VdfRVlZGaZPn4779+9jxIgRaNiwIT755BMMGzasJvpIREREZDC8YfJjjBs3DuPGjcPt27ehUChq7BFaRERERAZniKFXYw70Kri68s7dRERERE8qnQM9Pz8/iESVX2ly5coVvTpEREREVKMMcXsUY83oRUdHq7wuLS3FyZMnsXv3bkybNs1Q/SIiIiKqGXU4dLt8+XJ89NFHyMrKQuvWrbF06VI888wzj93ut99+Q/fu3dGmTRuNdz+pjM6B3ltvvaWx/PPPP0daWpquzRERERGZhOTkZERHR2P58uUIDQ3FypUrMWDAAJw/fx6NGzeudLv8/HyMGTMGvXv3xq1bt3TaZ7WedavJgAEDsGULHzJORERET7g6uo/e4sWLMXbsWERERKBly5ZYunQpvL29kZCQUOV2EyZMwIgRIxAcHKzzPg0W6G3evBnOzs6Gao6IiIioRhjyEWgymUxlKS4u1rjPkpISnDhxAmFhYSrlYWFhOHToUKV9XbNmDf7++2/MmTOnWu9V56HbDh06qFyMIQgCsrOzkZubi+XLl1erE0RERET1kbe3t8rrOXPmYO7cuWr1bt++DblcDg8PD5VyDw8PZGdna2z78uXLeOedd3DgwAFYWFTvRik6b/XCCy+ovDYzM4Obmxt69OiBFi1aVKsTRERERPVRRkYGHBwclK+trKyqrP/onUsEQdB4NxO5XI4RI0bg/fffR0BAQLX7p1OgV1ZWBl9fX/Tr1w+enp7V3ikRERFRnTHgVbcODg4qgV5lXF1dYW5urpa9y8nJUcvyAUBBQQHS0tJw8uRJvPHGGwAAhUIBQRBgYWGBn376Cb169XrsfnWao2dhYYHXX3+90vFnIiIioiedIefoaUssFiMwMBApKSkq5SkpKQgJCVGr7+DggDNnzuDUqVPKJTIyEs2bN8epU6fQuXNnrfar89Bt586dcfLkSfj4+Oi6KREREZHJmjx5MkaPHo2goCAEBwfjiy++wPXr1xEZGQkAmDlzJm7cuIF169bBzMwMbdq0Udne3d0dEolErbwqOgd6EydOxJQpU5CZmYnAwEDY2tqqrG/Xrp2uTRIRERHVrjp4ssXQoUNx584dxMbGIisrC23atMGuXbuUybOsrCxcv37doPsUCYKg1Vt97bXXsHTpUjg6Oqo3IhIpJxPK5XKDdpBIWzKZDFKpFL2bRsPCvOrJsFT/7UrlfTtNSb8GT9V1F6gWlAmlSMU25OfnazXvTVcVnxNNZ8TB3EqiV1vy4iL8tWBWjfXVULTO6K1duxYffvghrl69WpP9ISIiIiID0TrQq0j8cW4eERER1WfVuZhCUxv1gU5z9DTd54WIiIioXjHg7VWedDoFegEBAY8N9u7evatXh4iIiIjIMHQK9N5//31IpdKa6gsRERFRjePQbSWGDRsGd3f3muoLERERUc0zoaFbrZ+Mwfl5RERERPWLzlfdEhEREdVrJpTR0zrQUygUNdkPIiIiolrBOXpERERExsqEMnpaz9EjIiIiovqFGT0iIiIyLSaU0WOgR0RERCbFlOboceiWiIiIyEgxo0dERESmhUO3RERERMaJQ7dEREREVO8xo0dERESmhUO3REREREbKhAI9Dt0SERERGSlm9IiIiMikiP5d9G2jPmCgR0RERKbFhIZuGegRERGRSeHtVYiIiIio3mNGj4iIiEwLh26JiIiIjFg9CdT0xaFbIiIiIiPFjB4RERGZFFO6GIOBHhEREZkWE5qjx6FbIiIiIiPFjB4RERGZFA7dEhERERkrDt0SERERUX3HjB4ZnYvRjjCzltR1N6iG+e2KqOsuUC0KQFpdd4GMCIduiYiIiIyVCQ3dMtAjIiIi02JCgR7n6BEREREZKWb0iIiIyKRwjh4RERGRseLQLRERERHVd8zoERERkUkRCQJEgn4pOX23ry0M9IiIiMi0cOiWiIiIiOo7ZvSIiIjIpPCqWyIiIiJjxaFbIiIiIqrvmNEjIiIik8KhWyIiIiJjZUJDtwz0iIiIyKSYUkaPc/SIiIiIjBQzekRERGRaOHRLREREZLzqy9Crvjh0S0RERGSkmNEjIiIi0yII5Yu+bdQDDPSIiIjIpPCqWyIiIiKq95jRIyIiItPCq26JiIiIjJNIUb7o20Z9wKFbIiIiIiPFjB4RERGZFg7dEhERERknU7rqloEeERERmRYTuo8e5+gRERERGSlm9IiIiMikcOiWiIiIyFiZ0MUYHLolIiIiMlLM6BEREZFJ4dAtERERkbHiVbdEREREVN8xo0dEREQmhUO3RERERMaKV90SERERkaEtX74cfn5+kEgkCAwMxIEDByqtu3XrVvTt2xdubm5wcHBAcHAw9uzZo9P+GOgRERGRSakYutV30VVycjKio6MRExODkydP4plnnsGAAQNw/fp1jfX379+Pvn37YteuXThx4gR69uyJgQMH4uTJk1rvk0O3REREZFoUQvmibxs6Wrx4McaOHYuIiAgAwNKlS7Fnzx4kJCQgPj5erf7SpUtVXsfFxWHbtm3YsWMHOnTooNU+mdEjIiIi0yIYaAEgk8lUluLiYo27LCkpwYkTJxAWFqZSHhYWhkOHDmnVbYVCgYKCAjg7O2v9VhnoEREREVWTt7c3pFKpctGUmQOA27dvQy6Xw8PDQ6Xcw8MD2dnZWu3r448/RmFhIV5++WWt+8ehWyIiIjIpIhjg9ir//puRkQEHBwdluZWVVdXbiUQqrwVBUCvTZOPGjZg7dy62bdsGd3d3rfvJQI+IiIhMiwGfjOHg4KAS6FXG1dUV5ubmatm7nJwctSzfo5KTkzF27Fh8++236NOnj07d5NAtERERUQ0Ti8UIDAxESkqKSnlKSgpCQkIq3W7jxo0IDw/Hhg0b8Nxzz+m8X2b0iIiIyKTU1ZMxJk+ejNGjRyMoKAjBwcH44osvcP36dURGRgIAZs6ciRs3bmDdunUAyoO8MWPG4JNPPkGXLl2U2UBra2tIpVKt9slAj4iIiExLHT0ZY+jQobhz5w5iY2ORlZWFNm3aYNeuXfDx8QEAZGVlqdxTb+XKlSgrK8OkSZMwadIkZfkrr7yCpKQkrfbJQI+IiIiolkycOBETJ07UuO7R4C01NVXv/THQIyIiIpMiEgSI9LwYQ9/tawsDPSIiIjItin8XfduoB3jVLREREZGRYkaPiIiITAqHbomIiIiMVR1ddVsXGOgRERGRaTHgkzGedJyjR0RERGSkmNEjIiIik1JXT8aoCwz0iJ5w0r05cN6TDfO8UpQ0sEbuMG88CLDXWNf6Txm8F11SK786rzVKvaxruqtkADzf9PwrtzHk9Vw4u5fi2iUJVsxugLPH7Oq6W8bFhIZuGegRPcHsjt2F+6YM3BrZGEVN7SDdn4uGn1xGemxrlLlYVbrd1fltoLA2V76W2/NXvT7g+abug+4h8v2b+GxWQ5w7ZovnRt/B/PVXMa5Hc+TeENd196ge4hw9DUQiUZVLeHi4su7OnTvRo0cP2Nvbw8bGBh07dlR5hMnp06dhZWWF7du3q+xjy5YtkEgkOHv2LABg7ty5eOqpp1TqyGQyxMTEoEWLFpBIJPD09ESfPn2wdetWCJV8k5DL5YiPj0eLFi1gbW0NZ2dndOnSBWvWrFHWCQ8PV74XS0tL+Pv7Y+rUqSgsLAQApKenV/rejxw5AqD8MS2a1kskEpX+ZGdn480334S/vz+srKzg7e2NgQMH4pdfflHW8fX1xdKlS9Xei6ZjYmqcUm4hv6srZN3c/s3uNEapkxiOqblVbid3sIBcaqlcYCaqpR6TPni+6f/G38aejc7YvcEFGX9JsGJOQ+TetMTzY+7UddeMikhhmKU+4Nc+DbKyspT/T05OxuzZs3Hx4kVlmbV1+ZDIp59+iujoaMyYMQPLly+HWCzGtm3bEBkZibNnz2LRokVo37493nvvPYwfPx6hoaFwcXFBTk4OIiMj8f7776NNmzYa+5CXl4euXbsiPz8f8+fPR8eOHWFhYYF9+/Zh+vTp6NWrFxwdHdW2mzt3Lr744gt89tlnCAoKgkwmQ1paGu7du6dSr3///lizZg1KS0tx4MABREREoLCwEAkJCco6P//8M1q3bq2ynYuLi/L/Dg4OKscFKA+SK6SnpyM0NBSOjo5YuHAh2rVrh9LSUuzZsweTJk3Cn3/+WdkpIAAoU0ByrRD3BniqFN9v7QDJ3/9UualP7HmISgWUeElw53kvPGjhUJM9JUPg+TZ5FpYKNGt3H8mfuauUn9hnj1ZBhXXUKyPFoVvT5un53x9aqVQKkUikUgYAGRkZmDJlCqKjoxEXF6csnzJlCsRiMaKiojBkyBB07twZM2fOxPbt2zFp0iRs2rQJEyZMQLNmzTB16tRK+zBr1iykp6fj0qVLaNCggbI8ICAAw4cPV8ucVdixYwcmTpyIIUOGKMvat2+vVs/Kykr5nkaMGIG9e/fi+++/Vwn0XFxc1N73wzQdl4dNnDgRIpEIx44dg62trbK8devWeO211yrdTlvFxcUoLi5WvpbJZHq3+SQx/6cMIgVQ5mCpUi53sIRFfqnGbcocxbg1xgdFPjYQlQlwOHwHjT6+hMxpzSud50VPBp5vcnCWw9wCyLut+tGcl2sBJ/eyOuoV1Xccuq2mzZs3o7S0VGOwNmHCBNjZ2WHjxo0AAHNzc6xduxbbtm3DiBEjsGfPHiQlJcHc3FxtWwBQKBTYtGkTRo4cqRLkVbCzs4OFheYY3dPTE7/++ityc6se6nmUtbU1Sks1f5hUx927d7F7925MmjRJJciroCkbqav4+HhIpVLl4u3trXebT6RHR+EEARBpHpor9ZQgv5sbin1sUdTEDjmjfFDYVgqnPdk1308yDJ5vk/dookgkQr25OW+9IRhoqQcY6FXTpUuXIJVK4eXlpbZOLBbD398fly79dzVcy5YtER0djY0bN2Lu3LkICAiotO3bt2/j3r17aNGihc79Wrx4MXJzc+Hp6Yl27dohMjISP/74Y5XbHDt2DBs2bEDv3r1VykNCQmBnZ6eyyOVy5fr8/Hy19WFhYQCAv/76C4IgaP0eZsyYodbWw5lSTWbOnIn8/HzlkpGRodW+6gu5nQUEM6hlc8wLylDmoH0yvsjfFpa3ih9fkeoUzzfJ7ppDXgY4ualm76SuZbiXywE4Q6p4BJq+S33An5waIgiCyny1f/75B8nJybCxscGBAwcwffr0KrcFVOe7aatVq1Y4e/YsTpw4gYMHD2L//v0YOHAgwsPDsWrVKmW9nTt3ws7ODmVlZSgtLcXgwYPx6aefqrSVnJyMli1bqpQ9nIW0t7fH77//rrK+Yv6iru9h2rRpKhe5AMCyZcuwf//+SrexsrKClVXlVyLWexZmKPKxhc15Gf552klZbHNehsKnHLVuxirjAeSOlo+vSHWL59vklZWa4fIfNni6WwEO7ZYqy5/uVoDDe6RVbElUOQZ61RQQEID8/HzcvHlTbXi1pKQEV65cQa9evZRl06ZNg1gsxqFDhxAcHIx169ZhzJgxGtt2c3ODk5MTLly4UK2+mZmZoWPHjujYsSPefvttfP311xg9ejRiYmLg5+cHAOjZsycSEhJgaWmJBg0awNJS/YPB29sbTZs2rXI/la1v1qwZRCIRLly4gBdeeOGxfXZ1dVVry9nZ+bHbGbt7fT3glXgVRb62KPK3hXR/LizvliCvhxsAwHVLJizySpE9tvy8OqbcQqmrGCUNrMvnbB25A/sT93Dz9SZ1+TZISzzftPULV0xbloFLf1jjQpotnh11B+4NS/HDOpfHb0za48UY9Dgvvvgipk+fjo8//hgff/yxyroVK1agsLAQw4cPBwCkpKRg1apVOHDgANq3b4+4uDhER0ejb9++God+zczMMHToUHz11VeYM2eOWiBZWFgIKyurSufpPapVq1bK7SrY2tpWGcTpy9nZGf369cPnn3+OqKgotXl6eXl5BpmnZ+z+6eSMnMIyuOy4CfP88hvo3nirmfKeaub5pbC4898wnahMAbdvMmGRVwLB0gzFDa1xI6opCts51tE7IF3wfNO+7U6wd5Jj5Nu34OxehmsXJXh3lB9yeA89wxIA6Ht7lPoR5zHQq67GjRtj4cKFmDp1KiQSCUaPHg1LS0ts27YNs2bNwpQpU9C5c2fIZDKMHTsWU6dORZcuXQAAUVFR2LJlC8aPH48dO3ZobD8uLg6pqano3LkzPvjgAwQFBcHS0hIHDhxAfHw8jh8/rjFQeumllxAaGoqQkBB4enri6tWrmDlzJgICAnSe83fnzh1kZ6tO6nZ0dFRe8SsIgtp6AHB3d4eZmRmWL1+OkJAQdOrUCbGxsWjXrh3KysqQkpKChISEamcsTU1+T3fk93TXuO7Wa34qr+8N8MK9AepfHqj+4PmmnWtdsXOta113w6gZYo4d5+iZgLfffhtNmjTBokWL8Mknn0Aul6N169ZISEjAq6++CgCIjo6GVCrF+++/r9zOzMwMa9asQfv27SsdwnVycsKRI0fw4YcfYv78+bh27RqcnJzQtm1bfPTRR5BKNc/X6NevHzZu3Ij4+Hjk5+fD09MTvXr1wty5c7XOAFbo06ePWtnGjRsxbNgwAOW3M9GUkczKyoKnpyf8/Pzw+++/44MPPsCUKVOQlZUFNzc3BAYGqtzGhYiIiGqGSKjsEQtE9YxMJoNUKkWjz+bCzFrzfQaJqH4KiEir6y5QLSgTSpGKbcjPz4eDg+Fv/F3xOdHrqXdgYa7fxXxl8mL8eurDGuuroTCjR0RERKbFhC7G4H30iIiIiIwUM3pERERkWhRQfwpNddqoBxjoERERkUkxpatuOXRLREREZKSY0SMiIiLTYkIXYzDQIyIiItNiQoEeh26JiIiIjBQzekRERGRaTCijx0CPiIiITAtvr0JERERknHh7FSIiIiKq95jRIyIiItPCOXpERERERkohACI9AzVF/Qj0OHRLREREZKSY0SMiIiLTwqFbIiIiImNlgEAP9SPQ49AtERERkZFiRo+IiIhMC4duiYiIiIyUQoDeQ6+86paIiIiI6hIzekRERGRaBEX5om8b9QADPSIiIjItnKNHREREZKQ4R4+IiIiI6jtm9IiIiMi0cOiWiIiIyEgJMECgZ5Ce1DgO3RIREREZKWb0iIiIyLRw6JaIiIjISCkUAPS8D56iftxHj0O3REREREaKGT0iIiIyLRy6JSIiIjJSJhToceiWiIiIyEgxo0dERESmxYQegcZAj4iIiEyKICggCPpdNavv9rWFgR4RERGZFkHQPyPHOXpEREREVJeY0SMiIiLTIhhgjl49yegx0CMiIiLTolAAIj3n2NWTOXocuiUiIiIyUszoERERkWnh0C0RERGRcRIUCgh6Dt3Wl9urcOiWiIiIyEgxo0dERESmhUO3REREREZKIQAi0wj0OHRLREREZKSY0SMiIiLTIggA9L2PXv3I6DHQIyIiIpMiKAQIeg7dCgz0iIiIiJ5AggL6Z/R4exUiIiIiesjy5cvh5+cHiUSCwMBAHDhwoMr6+/btQ2BgICQSCfz9/bFixQqd9sdAj4iIiEyKoBAMsugqOTkZ0dHRiImJwcmTJ/HMM89gwIABuH79usb6V69exbPPPotnnnkGJ0+exKxZsxAVFYUtW7ZovU8GekRERGRaBIVhFh0tXrwYY8eORUREBFq2bImlS5fC29sbCQkJGuuvWLECjRs3xtKlS9GyZUtERETgtddew6JFi7TeJ+fokdGomBireFBUxz0hIkMrE0rrugtUC8pQfp5r+kKHMpTqfb/kir7KZDKVcisrK1hZWanVLykpwYkTJ/DOO++olIeFheHQoUMa93H48GGEhYWplPXr1w+JiYkoLS2FpaXlY/vJQI+MRkFBAQDg5rQP67gnRGRomXXdAapVBQUFkEqlBm9XLBbD09MTB7N3GaQ9Ozs7eHt7q5TNmTMHc+fOVat7+/ZtyOVyeHh4qJR7eHggOztbY/vZ2dka65eVleH27dvw8vJ6bB8Z6JHRaNCgATIyMmBvbw+RSFTX3ak1MpkM3t7eyMjIgIODQ113h2oQz7XpMNVzLQgCCgoK0KBBgxppXyKR4OrVqygpKTFIe4IgqH3eaMrmPezR+praeFx9TeWVYaBHRsPMzAyNGjWq627UGQcHB5P6QDBlPNemwxTPdU1k8h4mkUggkUhqdB+auLq6wtzcXC17l5OTo5a1q+Dp6amxvoWFBVxcXLTaLy/GICIiIqphYrEYgYGBSElJUSlPSUlBSEiIxm2Cg4PV6v/0008ICgrSan4ewECPiIiIqFZMnjwZq1atwurVq3HhwgW8/fbbuH79OiIjIwEAM2fOxJgxY5T1IyMjce3aNUyePBkXLlzA6tWrkZiYiKlTp2q9Tw7dEtVzVlZWmDNnzmPnhVD9x3NtOniujdPQoUNx584dxMbGIisrC23atMGuXbvg4+MDAMjKylK5p56fnx927dqFt99+G59//jkaNGiAZcuW4cUXX9R6nyKhvjysjYiIiIh0wqFbIiIiIiPFQI+IiIjISDHQIyIiIjJSDPSIiIiIjBQDPaJ/hYeH44UXXlArT01NhUgkQl5entq65s2bQywW48aNGyp1q1qSkpKqrFfZo3AAYMuWLejcuTOkUins7e3RunVrTJkyRbk+KSlJpS0vLy+8/PLLuHr1qrKOr6+vxv1++GH5o+PS09Mr7duRI0eU7ZSUlGDhwoVo3749bGxs4OrqitDQUKxZswalpaXVPqZVnY/NmzdDIpFg4cKFAIC5c+dq7GeLFi2U2/To0QPR0dEqr0UiETZt2qTS9tKlS+Hr61vpsaxYqrrRalXvy9fXF0uXLlUrj4uLg7m5ufL4V9St6meoR48eVdZ7uK1HXblyBcOHD0eDBg0gkUjQqFEjDB48GJcuXVLWebgte3t7BAUFYevWrcr12h53TXUqbiNRYe/evXj22Wfh4uICGxsbtGrVClOmTFH7ndL2mD7u9y88PFxZd+fOnejRowfs7e1hY2ODjh07IikpSbn+9OnTsLKywvbt21X2sWXLFkgkEpw9e1Z5PJ566imVOjKZDDExMWjRogUkEgk8PT3Rp08fbN26tdLnuMrlcsTHx6NFixawtraGs7MzunTpgjVr1ijrhIeHK9+LpaUl/P39MXXqVBQWFgLQ7vdX25/t7OxsvPnmm/D394eVlRW8vb0xcOBA/PLLL1Weg8qOCdUN3l6FqJoOHjyIoqIiDBkyBElJSYiJiUFISAiysrKUdd566y3IZDKVP9RSqRRHjx4FAFy8eFHtrvfu7u4a9/fzzz9j2LBhiIuLw6BBgyASiXD+/HmVP7pA+Z30L168CEEQ8Oeff2LChAkYNGgQTp06BXNzcwBAbGwsxo0bp7Kdvb292v5at26tUlZxJ/aSkhL069cPp0+fxrx58xAaGgoHBwccOXIEixYtQocOHQz+R37VqlWYNGkSPv/8c0RERCjLW7dujZ9//lmlroVF1X/aJBIJ3n33Xbz44otV3nS04lg+zNCP11uzZg2mT5+O1atXKx92fvz4ccjlcgDAoUOH8OKLL6r8rIjFYuX22pzLCiUlJejbty9atGiBrVu3wsvLC5mZmdi1axfy8/PV+tW/f3/k5eXho48+wpAhQ3Dw4EEEBwcD0O64jxs3DrGxsSplNjY2yv+vXLkSEydOxCuvvIItW7bA19cX169fx7p16/Dxxx9j8eLFVR88DR7+/UtOTsbs2bNVzqG1tTUA4NNPP0V0dDRmzJiB5cuXQywWY9u2bYiMjMTZs2exaNEitG/fHu+99x7Gjx+P0NBQuLi4ICcnB5GRkXj//ffRpk0bjX3Iy8tD165dkZ+fj/nz56Njx46wsLDAvn37MH36dPTq1QuOjo5q282dOxdffPEFPvvsMwQFBUEmkyEtLQ337t1Tqde/f3/lF6oDBw4gIiIChYWFSEhIUNap6vcXePzPdnp6OkJDQ+Ho6IiFCxeiXbt2KC0txZ49ezBp0iT8+eeflZ0CesIw0COqpsTERIwYMQLdu3fHpEmTMGvWLOUDsytYW1ujuLhYpexh7u7uGv/ga7Jz50507doV06ZNU5YFBASoZb1EIpFyf15eXpgzZw5GjRqFv/76C82bNwdQHghU1qcKLi4uldZZunQp9u/fj7S0NHTo0EFZ7u/vjyFDhhjsOZIVFi5ciNmzZ2PDhg1q94+ysLB47Ht51PDhw7Fjxw58+eWXmDhxYqX1Hj6WNWHfvn148OABYmNjsW7dOuzfvx/dunWDm5ubso6zszOAyn9WtDmXFc6fP48rV67g119/Vd63y8fHB6GhoWp1HR0d4enpCU9PT6xYsQKbNm3C9u3blYGeNsfdxsam0jqZmZmIiopCVFQUlixZoiz39fVFt27dqsz2VuXh/UmlUo3nMCMjA1OmTEF0dDTi4uKU5VOmTIFYLEZUVBSGDBmCzp07Y+bMmdi+fTsmTZqETZs2YcKECWjWrFmVN6ydNWsW0tPTcenSJZVntgYEBGD48OGVZoV37NiBiRMnYsiQIcqy9u3bq9WzsrJSvqcRI0Zg7969+P7771UCvap+f4HH/2xPnDgRIpEIx44dg62trbK8devWeO211yrdjp48HLolqoaCggJ8++23GDVqFPr27YvCwkKkpqbW6D49PT1x7tw55XCRtioyGBXDqYawfv169OnTRyXIq2BpaanywaCvd955B/PmzcPOnTt1ukloVRwcHDBr1izExsYqh7zqQmJiIoYPHw5LS0sMHz4ciYmJNbo/Nzc3mJmZYfPmzcqMoTYsLS1hYWFh0J+hb7/9FiUlJZg+fbrG9dp+AaqOzZs3o7S0VGOwNmHCBNjZ2WHjxo0AAHNzc6xduxbbtm3DiBEjsGfPHiQlJSmz449SKBTYtGkTRo4cqRLkVbCzs6s04+zp6Ylff/0Vubm5Or0fa2trg56bu3fvYvfu3Zg0aZLG3+WaPDdkeAz0iB6yc+dO2NnZqSwDBgxQq7dp0yY0a9YMrVu3hrm5OYYNG1atD+lGjRqp7Ksi46bJm2++iY4dO6Jt27bw9fXFsGHDsHr1ahQXF1e6TWZmJj766CM0atQIAQEByvIZM2aovc9HA9WQkBC1OhXBweXLl1XmY1VF22OqyY8//ogFCxZg27Zt6NOnj8Y6Z86cUWv/4aHdykycOBESiaTK4cH8/Hy1tsPCwh7b9qPn1c7OTuVu90D5HK4tW7Zg1KhRAIBRo0Zh8+bNkMlkj23/YdqcywoNGzbEsmXLMHv2bDg5OaFXr16YN28erly5Umn7xcXFmD9/PmQyGXr37q0s1+a4L1++XK3O2rVrAZT/DDk4OMDLy0ur96nNMdXWpUuXIJVKNe5bLBbD399fZc5iy5YtER0djY0bN2Lu3Lkqv0uPun37Nu7du6f178fDFi9ejNzcXHh6eqJdu3aIjIzEjz/+WOU2x44dw4YNG1TODVD17y9Q9c/2X3/9BUEQtH4Pmn4GH86UUt3i0C3RQ3r27Kky/AEAR48eVX4YV0hMTFQpGzVqlHK4SZdvuwcOHFCZT1XV3DJbW1v88MMP+Pvvv7F3714cOXIEU6ZMwSeffILDhw8r5z5V/AEXBAH379/H008/ja1bt6rM65o2bZrKpHSgPAh4WHJyMlq2bKlSVpHFEARB67lq2h5TTdq1a4fbt29j9uzZ6Nixo8a5Z82bN1ebLF/ZHLWHWVlZITY2Fm+88QZef/11jXXs7e3x+++/q5RVZEir8uh5BaC8gKLChg0b4O/vrxyae+qpp+Dv749NmzZh/Pjxj91HBW3O5cMmTZqEMWPGYO/evTh69Ci+/fZbxMXFYfv27ejbt6+y3vDhw2Fubo4HDx5AKpVi0aJFKgG6Nsd95MiRiImJUSmrmIOqy88QoN0xNZRH+/bPP/8gOTkZNjY2OHDgQKVZyIptgerN5WzVqhXOnj2LEydO4ODBg9i/fz8GDhyI8PBwrFq1Slmv4stTWVkZSktLMXjwYHz66acqbVX1+wtU/bOt63vQ9DO4bNky7N+/X6vtqWYx0CN6iK2tLZo2bapSlpmZqfL6/PnzOHr0KI4fP44ZM2Yoy+VyOTZu3Fhp0KCJn5+fzsMgTZo0QZMmTRAREYGYmBgEBAQgOTkZr776KoD//oCbmZnBw8ND49CLq6ur2vt8lLe3d6V1AgICcOHCBa36q80xrUzDhg2xZcsW9OzZE/3798fu3bvVPuzFYvFj30tlRo0ahUWLFmH+/PkqV9xWMDMzq1bbms7ro0H86tWrce7cOZVyhUKBxMREnQI9bc7lo+zt7TFo0CAMGjQI8+fPR79+/TB//nyVQG/JkiXo06cPHBwcNF4gpM1xl0qlVf4M5efnIysrS6usnjbHVFsV+75586ba8GpJSQmuXLmCXr16KcumTZsGsViMQ4cOITg4GOvWrVN58PzD3Nzc4OTkpPXvx6PMzMzQsWNHdOzYEW+//Ta+/vprjB49GjExMfDz8wPw35cnS0tLNGjQQOMFRVX9/lbsp7L1zZo1g0gkwoULFzReNf8oTT+DFXNLqe5x6JZIR4mJiejWrRtOnz6NU6dOKZfp06fX+ByrR/n6+sLGxkZlnlnFH3B/f3+DzpV72IgRI/Dzzz/j5MmTauvKysoMOu+tcePG2LdvH3JychAWFqbz0GZVzMzMEBcXh4SEBKSnpxus3cc5c+YM0tLSkJqaqvIztH//fhw/flzneZj6qLgtyqPnzNPTE02bNq30KnB9vfTSSxCLxcpb5TyquhdjaOPFF1+EhYUFPv74Y7V1K1asQGFhIYYPHw4ASElJwapVq5CUlIT27dsjLi4O0dHRKlf3PszMzAxDhw7F+vXrcfPmTbX1hYWFKCsr07qvrVq1Um5XoeLLk4+PT5VXjVeXs7Mz+vXrh88//1zj73JNnhsyPGb0iHRQWlqKr776CrGxsWq3VoiIiMDChQtx+vRpjVfKaZKTk4OioiKVMhcXF41/vOfOnYv79+/j2WefhY+PD/Ly8rBs2TKUlpaqZGK0UVBQoHa/PhsbG5Vbvdy5c0etjqOjIyQSCaKjo/HDDz+gd+/emDdvHrp27Qp7e3ukpaVhwYIFSExMNOjtVRo1aoTU1FT07NkTYWFh2LNnD6RSKYDywPLRfopEInh4eGjV9vPPP4/OnTtj5cqVatsIgqDxvobu7u4wM6v+9+TExER06tQJ3bp1U1sXHByMxMRElStRq6LNuaxw6tQpzJkzB6NHj0arVq0gFouxb98+rF69WiU7rQ1tjvv9+/fV6lhZWcHJyQne3t5YsmQJ3njjDchkMowZMwa+vr7IzMzEunXrYGdnpzEQM4TGjRtj4cKFmDp1KiQSCUaPHg1LS0ts27YNs2bNwpQpU9C5c2fIZDKMHTsWU6dORZcuXQAAUVFR2LJlC8aPH48dO3ZobD8uLg6pqano3LkzPvjgAwQFBcHS0hIHDhxAfHw8jh8/rjGT/9JLLyE0NBQhISHw9PTE1atXMXPmTAQEBOg856+q31/g8T/by5cvR0hICDp16oTY2Fi0a9cOZWVlSElJQUJCQrUzllQHBCISBEEQXnnlFWHw4MFq5Xv37hUACPfu3RM2b94smJmZCdnZ2RrbaNu2rfDmm29q3aam5fDhwxrb/vXXX4UXX3xR8Pb2FsRiseDh4SH0799fOHDggLLOmjVrBKlUWuX79PHx0bjfCRMmCIIgCFevXq20bxs3blS2U1RUJMTHxwtt27YVJBKJ4OzsLISGhgpJSUlCaWmp1se0Mpq2vXnzptC8eXOhY8eOwr1794Q5c+Zo7KeVlZVym+7duwtvvfVWpa8FQRAOHTokABB8fHxUjmVlxyErK0tjn6t6Xz4+PsKSJUuE4uJiwcXFRVi4cKHGNj7++GPB1dVVKC4u1qrNqs7lo3Jzc4WoqCihTZs2gp2dnWBvby+0bdtWWLRokSCXy5X1AAjfffedxjYEQdD6uGuq069fP5W2UlJShH79+glOTk6CRCIRWrRoIUydOlW4efOm1se0Mo/7fdi2bZvwzDPPCLa2toJEIhECAwOF1atXK9e/+uqrQps2bZTnosLly5cFGxsbYe3atcrj0b59e5U6eXl5wjvvvCM0a9ZM+fvap08f4bvvvhMUCoXG/nzxxRdCz549BTc3N0EsFguNGzcWwsPDhfT0dGWdyn6nKmjz+6vtz/bNmzeFSZMmCT4+PoJYLBYaNmwoDBo0SNi7d6+yTmXnQNMxobohEoRKbtFNRERERPUa5+gRERERGSkGekRERERGioEeERERkZFioEdERERkpBjoERERERkpBnpERERERoqBHhEREZGRYqBHREREZKQY6BERGdDcuXNVHv8WHh6u1YPhDS09PR0ikQinTp2qtI6vry+WLl2qdZtJSUkaH92lK5FIhO+//17vdojo8RjoEZHRCw8Ph0gkgkgkgqWlJfz9/TF16lSND2w3tE8++QRJSUla1dUmOCMi0oVFXXeAiKg29O/fH2vWrEFpaSkOHDiAiIgIFBYWIiEhQa1uaWkpLC0tDbJfqVRqkHaIiKqDGT0iMglWVlbw9PSEt7c3RowYgZEjRyqHDyuGW1evXg1/f39YWVlBEATk5+dj/PjxcHd3h4ODA3r16oXTp0+rtPvhhx/Cw8MD9vb2GDt2LIqKilTWPzp0q1AosGDBAjRt2hRWVlZo3LgxPvjgAwCAn58fAKBDhw4QiUTo0aOHcrs1a9agZcuWkEgkaNGiBZYvX66yn2PHjqFDhw6QSCQICgrCyZMndT5GixcvRtu2bWFrawtvb29MnDgR//zzj1q977//HgEBAZBIJOjbty8yMjJU1u/YsQOBgYGQSCTw9/fH+++/j7KyMp37Q0T6Y6BHRCbJ2toapaWlytd//fUXvvnmG2zZskU5dPrcc88hOzsbu3btwokTJ/D000+jd+/euHv3LgDgm2++wZw5c/DBBx8gLS0NXl5eagHYo2bOnIkFCxbgvffew/nz57FhwwZ4eHgAKA/WAODnn39GVlYWtm7dCgD48ssvERMTgw8++AAXLlxAXFwc3nvvPaxduxYAUFhYiOeffx7NmzfHiRMnMHfuXEydOlXnY2JmZoZly5bh7NmzWLt2LX799VdMnz5dpc79+/fxwQcfYO3atfjtt98gk8kwbNgw5fo9e/Zg1KhRiIqKwvnz57Fy5UokJSUpg1kiqmUCEZGRe+WVV4TBgwcrXx89elRwcXERXn75ZUEQBGHOnDmCpaWlkJOTo6zzyy+/CA4ODkJRUZFKW02aNBFWrlwpCIIgBAcHC5GRkSrrO3fuLLRv317jvmUymWBlZSV8+eWXGvt59epVAYBw8uRJlXJvb29hw4YNKmXz5s0TgoODBUEQhJUrVwrOzs5CYWGhcn1CQoLGth7m4+MjLFmypNL133zzjeDi4qJ8vWbNGgGAcOTIEWXZhQsXBADC0aNHBUEQhGeeeUaIi4tTaeerr74SvLy8lK8BCN99912l+yUiw+EcPSIyCTt37oSdnR3KyspQWlqKwYMH49NPP1Wu9/HxgZubm/L1iRMn8M8//8DFxUWlnQcPHuDvv/8GAFy4cAGRkZEq64ODg7F3716Nfbhw4QKKi4vRu3dvrfudm5uLjIwMjB07FuPGjVOWl5WVKef/XbhwAe3bt4eNjY1KP3S1d+9exMXF4fz585DJZCgrK0NRUREKCwtha2sLALCwsEBQUJBymxYtWsDR0REXLlxAp06dcOLECRw/flwlgyeXy1FUVIT79++r9JGIah4DPSIyCT179kRCQgIsLS3RoEEDtYstKgKZCgqFAl5eXkhNTVVrq7q3GLG2ttZ5G4VCAaB8+LZz584q68zNzQEAgiBUqz8Pu3btGp599llERkZi3rx5cHZ2xsGDBzF27FiVIW6g/PYoj6ooUygUeP/99/F///d/anUkEone/SQi3TDQIyKTYGtri6ZNm2pd/+mnn0Z2djYsLCzg6+ursU7Lli1x5MgRjBkzRll25MiRStts1qwZrK2t8csvvyAiIkJtvVgsBlCeAavg4eGBhg0b4sqVKxg5cqTGdlu1aoWvvvoKDx48UAaTVfVDk7S0NJSVleHjjz+GmVn59O1vvvlGrV5ZWRnS0tLQqVMnAMDFixeRl5eHFi1aACg/bhcvXtTpWBNRzWGgR0SkQZ8+fRAcHIwXXngBCxYsQPPmzXHz5k3s2rULL7zwAoKCgvDWW2/hlVdeQVBQELp27Yr169fj3Llz8Pf319imRCLBjBkzMH36dIjFYoSGhiI3Nxfnzp3D2LFj4e7uDmtra+zevRuNGjWCRCKBVCrF3LlzERUVBQcHBwwYMADFxcVIS0vDvXv3MHnyZIwYMQIxMTEYO3Ys3n33XaSnp2PRokU6vd8mTZqgrKwMn376KQYOHIjffvsNK1asUKtnaWmJN998E8uWLYOlpSXeeOMNdOnSRRn4zZ49G88//zy8vb0xZMgQmJmZ4Y8//sCZM2cwf/583U8EEemFV90SEWkgEomwa9cudOvWDa+99hoCAgIwbNgwpKenK6+SHTp0KGbPno0ZM2YgMDAQ165dw+uvv15lu++99x6mTJmC2bNno2XLlhg6dChycnIAlM9/W7ZsGVauXIkGDRpg8ODBAICIiAisWrUKSUlJaNu2Lbp3746kpCTl7Vjs7OywY8cOnD9/Hh06dEBMTAwWLFig0/t96qmnsHjxYixYsABt2rTB+vXrER8fr1bPxsYGM2bMwIgRIxAcHAxra2ts2rRJub5fv37YuXMnUlJS0LFjR3Tp0gWLFy+Gj4+PTv0hIsMQCYaY3EFERERETxxm9IiIiIiMFAM9IiIiIiPFQI+IiIjISDHQIyIiIjJSDPSIiIiIjBQDPSIiIiIjxUCPiIiIyEgx0CMiIiIyUgz0iIiIiIwUAz0iIiIiI8VAj4iIiMhI/T8c0A3cbKxE1QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Section 28\n",
    "cm_plot, classification_report, metrics = plot_count_and_normalized_confusion_matrix(\n",
    "    y_true, y_pred, display_labels, labels, xticks_rotation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "75c64dc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 29\n",
    "# Log metrics\n",
    "for metric_name, metric_value in metrics.items():\n",
    "    wandb.log({metric_name: metric_value})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4aaa4209",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 31\n",
    "# Log the confusion matrix matplotlib figure\n",
    "wandb.log({'confusion_matrix': wandb.Image(cm_plot)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "45aaca05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact classification_report_ds_5_t_1_trn_250>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 32\n",
    "# Log the classification report as an artifact\n",
    "classification_report = (pd.DataFrame({k: v for k, v in classification_report.items() if k != 'accuracy'})\n",
    "                         .transpose().reset_index())\n",
    "\n",
    "wandb.log({'classification_report': wandb.Table(\n",
    "    dataframe=classification_report)})\n",
    "\n",
    "classification_report_artifact = wandb.Artifact(\n",
    "      f'classification_report_{model_name}', type='classification_report')\n",
    "\n",
    "with classification_report_artifact.new_file('classification_report.txt', mode='w') as f:\n",
    "    f.write(pprint.pformat(classification_report))\n",
    "\n",
    "wandb.run.log_artifact(classification_report_artifact)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6db7bbf2",
   "metadata": {},
   "source": [
    "## Evaluation on Second Evaluation Set\n",
    "In this section, we will evaluate the performance of our model on a second evaluation set (`second_eval_set`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8cbb7ff7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['ds_5__task_1_eval_set', 'ds_x__task_1_full_eval', 'ds_5__task_1_train_set_250'])\n"
     ]
    }
   ],
   "source": [
    "print(datasets.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1bf1b642",
   "metadata": {},
   "outputs": [],
   "source": [
    "second_eval_set_name = f'ds_{dataset_eval}__task_{task}_full_eval'\n",
    "full_eval_df = datasets[second_eval_set_name]\n",
    "for df_name, df in datasets.items():\n",
    "    if df_name == second_eval_set_name:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9cfc903c",
   "metadata": {},
   "outputs": [],
   "source": [
    "full_eval_df = datasets[second_eval_set_name]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "303aeed5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##################################################\n",
      "Getting predictions on the evaluation set\n"
     ]
    }
   ],
   "source": [
    "# Load the second evaluation set (second_eval_set)\n",
    "# Evaluate the model on the evaluation set and store the predictions\n",
    "print(\"\\n\" + \"#\" * 50)\n",
    "print(\"Getting predictions on the evaluation set\")\n",
    "predictions_2 = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7c218926",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 494/494 [06:55<00:00,  1.19it/s]  \n"
     ]
    }
   ],
   "source": [
    "for messages in tqdm(full_eval_df['openai_instance_without_completion'].tolist()):\n",
    "    # Retry the completion at least COMPLETION_RETRIES times\n",
    "    num_retries = 2\n",
    "    response = None\n",
    "    while num_retries < COMPLETION_RETRIES and response is None:\n",
    "        try:\n",
    "            response = client.chat.completions.create(\n",
    "                model=full_model_name,\n",
    "                messages=messages,\n",
    "                temperature=temp,\n",
    "                n=1\n",
    "            )\n",
    "        except Exception as e:\n",
    "            print('Error getting predictions. Retrying...')\n",
    "            time.sleep(5)\n",
    "            num_retries += 1\n",
    "            if num_retries >= COMPLETION_RETRIES:\n",
    "                print('Maximum amount of retires reached')\n",
    "                raise e\n",
    "    response_dict = response.to_dict()\n",
    "    predictions_2.append(response_dict['choices'][0]['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "789fce14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add predictions to df\n",
    "full_eval_df['prediction'] = predictions_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "616c4658",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_output_dir_2 = os.path.join(output_dir, 'predictions_2',\n",
    "                                      f'dataset_{dataset}_task_{task}')\n",
    "os.makedirs(predictions_output_dir_2, exist_ok=True)\n",
    "datasets[second_eval_set_name].to_csv(\n",
    "    os.path.join(predictions_output_dir_2, f\"{model_name}-{run_name}.csv\"),\n",
    "    index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ec8481ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get performance metrics\n",
    "y_true_2 = full_eval_df['completion_label']\n",
    "y_pred_2 = full_eval_df['prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "296deb14",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_type_2 = 'full_name' if not not_use_full_labels else 'short_name'\n",
    "display_labels_2 = task_to_display_labels[task][label_type]\n",
    "labels_2 = display_labels_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "83dbd847",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\maelk\\miniconda3\\envs\\chatGPT\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\maelk\\miniconda3\\envs\\chatGPT\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\maelk\\miniconda3\\envs\\chatGPT\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HATE SPEECH': {'f1-score': 0.6084507042253521,\n",
      "                 'precision': 0.5118483412322274,\n",
      "                 'recall': 0.75,\n",
      "                 'support': 144},\n",
      " 'KEINE HATE SPEECH': {'f1-score': 0.8089053803339517,\n",
      "                       'precision': 0.7703180212014135,\n",
      "                       'recall': 0.8515625,\n",
      "                       'support': 256},\n",
      " 'TOXIC SPEECH': {'f1-score': 0.0,\n",
      "                  'precision': 0.0,\n",
      "                  'recall': 0.0,\n",
      "                  'support': 94},\n",
      " 'accuracy': 0.659919028340081,\n",
      " 'macro avg': {'f1-score': 0.4724520281864346,\n",
      "               'precision': 0.42738878747788034,\n",
      "               'recall': 0.5338541666666666,\n",
      "               'support': 494},\n",
      " 'weighted avg': {'f1-score': 0.5965519813237699,\n",
      "                  'precision': 0.548395899929155,\n",
      "                  'recall': 0.659919028340081,\n",
      "                  'support': 494}}\n"
     ]
    },
    {
     "data": {
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/2rvz+JquvY/j35PxJCQxS1RkQMxFxRBUUWNvW56rrqGolqpSqqWUXEMNUYqq3gqtEHpraGlrqFtP2qJUKSk6SHVAcSWP4SIaZNzPH25OHTmJczgRyfm8X6/zejlrr7322tPJz9prrb2iwZ2qJO6QrEw3/fKdr+5rc0m7Pv3zJUT3tbmkr7cEFLAmbpfJMGRywvAQZ5SBO484jzjvdrh5Sn51DF342k0VHvxznsHzu91Uvp3tmer9G+fobIKHsi9L7v8N6a78bpLcDHlVtv4dcfOWvCtfm8vw7GduqtipmMx+7yKyctyVlFJRUWEntfXncEt687CT2vZzqN3lmEyGvNw5tyUZcV7RckasV1ziPBoGb1FERIQuXryoU6dO5RkGkpGRoSNHjqh9+/aWtJdeekleXl7atWuXoqKitGLFCg0YMMBm2RUrVlTZsmWVlJR0S3Vzc3NT06ZN1bRpU73wwgv65z//qf79+ys6OlphYWGSpHbt2ik2Nlaenp6qUqWKPD0985QTHBysGjVqFLid/JbXrFlTJpNJSUlJ6t69+03rXKFChTxllStXrsB1vL295e3tfdOyS5JVO+/VkmEf64l23+rz76qrbvBpdW+epJnr2kiSfLwy9XTHffri+zCdu+SroLKX9GyXb3Txslnbfwgr4trDGYYPT1Tbdsc19ZXWunLFQ2XLXpEkpaV5KiPj2k966/tP6OJFb5057avQ0Isa+uy3+vrre/Ttt/nPFYXi68O3K+ilBSf083c+StpXSg/1O6dK92TqkxX5dDuBczDHYIlGnEecV9juGZClwxM8VbqeIf+GOUpe6670ZJOCel7rGXT0DQ9l/J9JtWKuDQGv9FC2ji/20M8TPVVtWJayzktH53kosHu25eUjqd+ZlHHapFK1DWX8n/R7rIeUI1V9krnI7jbvftNQMx75XD8mV9R3/w5Uj0aHFOR/SWv3X3sh0IgHdquSX5ombrrW07fXfT8oObW0jp0rI0lqXDVFA5od1OrEP19w5OGWreoVrr2IyMM9W5VKp6lWpbO6nOmpE+dpRCquiPOKkAvNMUjD4C3q0aOHxo4dq7lz52ru3LlWyxYtWqS0tDT16dNHkpSQkKAlS5Zox44datiwoWJiYjRq1Ch17NjR5hAVNzc39erVS++++64mT56cJyBNS0uTt7d3vvPP3Khu3bqW9XKVKlWqwGDwdpUrV06dO3fWW2+9pZEjR+aZf+bChQtOmX/G1SSdrKSxKzprWJc9GvRgok6d99PrG1tqy4FrQ5ZyckyqHnhOXe87LD9zhs5e8lXib1UU/V5HXc7wuknpKA4efuQ3SdLs17Zapc+d20yfJVz7D2G5clc0ZMh+lSmTrv/8x6zPPw/VqpVMTl1Sbd9QVn5ls/X4C/+ncpWy9Pths/7eL0yn/809D9wq4ryCEefdvopdcpR5IUvHF3so44xUqoah+m9lyPzfyyHjjEnpKX/2yHT3lRq8naHfZnroQB8veQRIFTtnK+S5Pxv9cjKkY//w0NWTJrn7SuVa56hWTIY8/O/03uFm/jephsr4XNUzrRJVoXSafj1TTs+9/xclp14bBVSx9GUF+f9hyW8yGRrZdrfuCbikrBw3nbzgrwXbmlsaEiWpkl+a1gz6wPL9iRYH9USLg9r3exUNXtntzu0cnIo4D3cCDYO3qFq1apo9e7bGjBkjs9ms/v37y9PTU+vXr9eECRM0evRoNW/eXKmpqRo0aJDGjBmjFi1aSJJGjhypdevWaciQIdq4caPN8mNiYrRt2zY1b95cM2bMUGRkpDw9PbVjxw7NnDlTe/futRlwPfbYY2rVqpVatmypwMBAHT16VOPHj1dERITDc9mcO3dOKSkpVmllypSxvCnPMIw8yyWpUqVKcnNz08KFC9WyZUs1a9ZMU6dO1b333qusrCwlJCQoNjb2lp+Uu7qvfgrRVz+F2FyWnuWh5+MevsM1wp3UtUuvm+bZsD5CG9bnP78VSp5Nyyto0/IKRV0N12JIcsYIruLxINnlEOcR590JVXpnq0pv268MrjU978tifMMMNXg7/5fIlIk0FPlxhtPqh8L1/rf19f639W0um/RJe6vvqxMbaHViwVPCnLror0Yzn3Va/XD3IM4rIs6I9YpJnEfD4G144YUXVL16dc2ZM0dvvPGGsrOzVa9ePcXGxurJJ5+UJI0aNUoBAQF65ZVXLOu5ublp2bJlatiwYb5DTcqWLavdu3fr1Vdf1fTp0/X777+rbNmyatCggV577TUFBNjuDt65c2etWrVKM2fO1MWLFxUYGKj27dtrypQpdj95ztWhQ4c8aatWrVLv3r0lXZvrxdaT8OTkZAUGBiosLEzffvutZsyYodGjRys5OVkVK1ZUkyZNFBsb61BdAAC4mzDHYMlHnEecBwBwXa40x6DJMIpJTYGbSE1NVUBAgBr3niF3L3NRVwdFoNzB80VdBRSxnO9+KuoqoIhkGZnapvW6ePGi/P0Lb9xc7t+a9o1flof77f+tycq+qi/2v1ro9QaKu9x7b/zXnWUunXfORJR8a99pf/NMKLEqv7mrqKuAInSn4jzJubFecYnz6DEIAADgKENOevnI7RcBAAAAJ3NGrFdM4jwaBgEAABzFW4kBAABKLhd6K7FbUVcAAAAAAAAAwJ1Hj0EAAABH5UgyOakcAAAA3F2cEesVkziPhkEAAAAH8VZiAACAksuV3krMUGIAAAAAAADABdFjEAAAwFG8fAQAAKDkcqGXj9AwCAAA4CgaBgEAAEouF2oYZCgxAAAAAAAAUMQWLlyosLAwmc1mNWnSRDt27Mg3b3Jysvr27atatWrJzc1No0aNuqVt0jAIAADgqNynyM742GnmzJlq2rSp/Pz8VKlSJXXv3l2HDx+2yjNw4ECZTCarT4sWLZy99wAAACXbHY7zJGnNmjUaNWqUoqOjtX//ft1///3q2rWrjh8/bjN/enq6KlasqOjoaDVs2PCWd5WGQQAAAEflOPFjp+3bt2v48OHavXu3EhISlJWVpU6dOiktLc0qX5cuXZScnGz5bN68+bZ2FQAAwOXc4ThPkubNm6dBgwZp8ODBqlOnjubPn6/g4GDFxsbazB8aGqo33nhDAwYMUEBAgOP7+F/MMQgAAFAMfPrpp1bfly1bpkqVKikxMVFt2rSxpHt7eyswMPBOVw8AAAA2pKamWn339vaWt7e3VVpGRoYSExP18ssvW6V36tRJu3btKtT60WMQAADAQSbDcNpHuhYwXv9JT0+/aR0uXrwoSSpXrpxV+rZt21SpUiVFRETo6aef1unTp51/AAAAAEowZ8Z5wcHBCggIsHxmzpyZZ3tnz55Vdna2KleubJVeuXJlpaSkFOq+0mMQAADAUU5+K3FwcLBV8uTJkzVlypQCVjP04osvqnXr1qpfv74lvWvXrurZs6dCQkJ09OhRTZw4Ue3bt1diYmKeJ9MAAADIhxPfSnzixAn5+/tbkguKyUwm0w1FGHnSnI2GQQAAgCLmSMAoSc8995y+++477dy50yq9V69eln/Xr19fkZGRCgkJ0SeffKK//vWvzq00AAAAbsrf398qzrOlQoUKcnd3z9M78PTp03l6ETobQ4kBAAAclWM476M/A8bcT0ENgyNGjNCGDRu0detWVa1atcBqBgUFKSQkRL/88otTdx8AAKBEc2KcZw8vLy81adJECQkJVukJCQlq2bKls/fOCj0GAQAAHOXkocT2ZTU0YsQIffTRR9q2bZvCwsJuus65c+d04sQJBQUF3U4tAQAAXIsThxLb68UXX1T//v0VGRmpqKgovf322zp+/LiGDh0qSRo/frz+/e9/a8WKFZZ1Dhw4IEn6448/dObMGR04cEBeXl6qW7eu3dulYRAAAKAYGD58uFauXKn169fLz8/PMtQkICBAPj4++uOPPzRlyhT16NFDQUFBOnbsmCZMmKAKFSrof/7nf4q49gAAAChIr169dO7cOU2dOlXJycmqX7++Nm/erJCQEElScnKyjh8/brVO48aNLf9OTEzUypUrFRISomPHjtm9XRoGAQAAHOakHoOyv4zY2FhJUtu2ba3Sly1bpoEDB8rd3V3ff/+9VqxYoQsXLigoKEjt2rXTmjVr5Ofn54S6AgAAuApnxHqOrz9s2DANGzbM5rL4+Pi8W3BCPErDIAAAgKOKaChxQXx8fLRly5bbrREAAACKYChxUeHlIwAAAAAAAIALoscgAACAo3IM3crwENvlAAAA4K7ijFivmMR5NAwCAAA4ysi59nFGOQAAALi7OCPWKyZxHkOJAQAAAAAAABdEj0EAAABHFcHLRwAAAHCHuNDLR2gYBAAAcBRzDAIAAJRcLjTHIEOJAQAAAAAAABdEj0EAAABHMZQYAACg5GIoMQAAAPJlyEkNg7dfBAAAAJzMGbFeMYnzGEoMAAAAAAAAuCB6DAIAADiKocQAAAAlF0OJAQAAkK+cHEk5TioHAAAAdxVnxHrFJM5jKDEAAAAAAADggugxCAAA4CiGEgMAAJRcDCUGAABAvmgYBAAAKLlcqGGQocQAAAAAAACAC6LHIAAAgKNyDElOeAqcUzyeJAMAALgUZ8R6xSTOo2EQAADAQYaRI8O4/TfNOaMMAAAAOJczYr3iEucxlBgAAAAAAABwQfQYBAAAcJRhOGd4SDGZlBoAAMClOCPWKyZxHg2DAAAAjjKcNMdgMQkYAQAAXIozYr1iEucxlBgAAAAAAABwQfQYBAAAcFROjmRywoTSxWRSagAAAJfijFivmMR5NAwCAAA4iqHEAAAAJRdDiQEAAAAAAACUZPQYBAAAcJCRkyPDCUOJjWIyxAQAAMCVOCPWKy5xHg2DAAAAjmIoMQAAQMnFUGIAAAAAAAAAJRk9BgEAAByVY0gmegwCAACUSM6I9YpJnEfDIAAAgKMMQ5IT5o0pJgEjAACAS3FGrFdM4jyGEgMAAAAAAAAuiB6DAAAADjJyDBlOGEpsFJMnyQAAAK7EGbFecYnzaBgEAABwlJEj5wwldkIZAAAAcC5nxHrFJM5jKDEAAEAxsnDhQoWFhclsNqtJkybasWNHUVcJAAAATuBonLd9+3Y1adJEZrNZ4eHhWrRokcPbpGEQAADAQUaO4bSPI9asWaNRo0YpOjpa+/fv1/3336+uXbvq+PHjhbSnAAAArqc4xHlHjx7VQw89pPvvv1/79+/XhAkTNHLkSK1bt86h7dIwCAAA4Cgjx3kfB8ybN0+DBg3S4MGDVadOHc2fP1/BwcGKjY0tpB0FAABwQcUgzlu0aJGqVaum+fPnq06dOho8eLCeeuopzZkzx6HtMscgSozciT2zM68WcU1QVLKy04u6CihiOUZmUVcBRSRL1879nZrkOUuZkhM2lVvv1NRUq3Rvb295e3tbpWVkZCgxMVEvv/yyVXqnTp20a9eu268McBfLvbfT07KKuCYoKtnpxPiuLIsYz6Xd6TjPss3b3Fxhx3lff/21OnXqZJXWuXNnxcXFKTMzU56ennbVk4ZBlBiXLl2SJH23bloR1wQAUFQuXbqkgICAQivfy8tLgYGB2pmy2Wllli5dWsHBwVZpkydP1pQpU6zSzp49q+zsbFWuXNkqvXLlykpJSXFafYC7UW6cN6/D50VcExSdLUVdARShQ0VdAdwVCjvOk5wf6xVmnJeSkmIzf1ZWls6ePaugoCC76kjDIEqMKlWq6MSJE/Lz85PJZCrq6txxqampCg4O1okTJ+Tv71/U1UER4Bpwba5+/g3D0KVLl1SlSpVC3Y7ZbNbRo0eVkZHhtDINw8jzd+vGp8jXuzGvrfWBkoY4z7V/48E14Opc/fzfqThPcn6sV9hxnq38ttILQsMgSgw3NzdVrVq1qKtR5Pz9/V3yjwX+xDXg2lz5/Bf2E+RcZrNZZrP5jmzrehUqVJC7u3uep8anT5/O87QYKGmI865x5d94XMM14Npc+fzfqThPKppY71bivMDAQJv5PTw8VL58ebu3zctHAAAAigEvLy81adJECQkJVukJCQlq2bJlEdUKAAAAt+tW4ryoqKg8+f/3f/9XkZGRds8vKNEwCAAAUGy8+OKLWrJkiZYuXaqkpCS98MILOn78uIYOHVrUVQMAAMBtuFmcN378eA0YMMCSf+jQofr999/14osvKikpSUuXLlVcXJzGjBnj0HYZSgyUEN7e3po8eXKB8xWgZOMacG2cf9fQq1cvnTt3TlOnTlVycrLq16+vzZs3KyQkpKirBqAQ8RsPrgHXxvl3DTeL85KTk3X8+HFL/rCwMG3evFkvvPCC3nrrLVWpUkULFixQjx49HNquybiT73sGAAAAAAAAcFdgKDEAAAAAAADggmgYBAAAAAAAAFwQDYMAAAAAAACAC6JhEAAAAAAAAHBBNAwCNgwcOFDdu3fPk75t2zaZTCZduHAhz7JatWrJy8tL//73v63yFvSJj48vMF9KSkq+dVy3bp2aN2+ugIAA+fn5qV69eho9erRleXx8vFVZQUFB+tvf/qajR49a8oSGhtrc7quvvipJOnbsWL512717t6WcjIwMzZ49Ww0bNpSvr68qVKigVq1aadmyZcrMzLzlY1oQW+WtXbtWZrNZs2fPliRNmTLFZt1r165tWadt27YaNWqU1XeTyaTVq1dblT1//nyFhoZavt94fHM/ZrM53zoXtK+hoaGaP39+nvSYmBi5u7tbzklu3oKuq7Zt2xaY7/qybnTkyBH16dNHVapUkdlsVtWqVdWtWzf9/PPPljzXl+Xn56fIyEh9+OGHluX2HndbeYYOHWpVn61bt+qhhx5S+fLl5evrq7p162r06NF57jNHjumNbnafDhw40JJ306ZNatu2rfz8/OTr66umTZsqPj7esvzgwYPy9vbWhg0brLaxbt06mc1m/fDDD5Zj1KhRI6s8qampio6OVu3atWU2mxUYGKgOHTroww8/VH7vCcvOztbMmTNVu3Zt+fj4qFy5cmrRooWWLVtmyTNw4EDLvnh6eio8PFxjxoxRWlqaJPvuc3uv95SUFI0YMULh4eHy9vZWcHCwHnnkEX3++ec3PS+2jgkAFAbiPOI84jziPOI84jz8yaOoKwCUBDt37tTVq1fVs2dPxcfHKzo6Wi1btlRycrIlz/PPP6/U1FSrH/KAgADt2bNHknT48GH5+/tblVupUiWb2/vss8/Uu3dvxcTE6NFHH5XJZNKhQ4esfpQlyd/fX4cPH5ZhGPrpp5/0zDPP6NFHH9WBAwfk7u4uSZo6daqefvppq/X8/PzybK9evXpWaeXLl5d0LVjs3LmzDh48qGnTpqlVq1by9/fX7t27NWfOHDVu3PiO/BFYsmSJhg8frrfeekuDBw+2pNerV0+fffaZVV4Pj4J/+sxms/7+97+rR48e8vT0zDdf7vG9nslkuoXa52/ZsmUaO3asli5dqpdfflmStHfvXmVnZ0uSdu3apR49elhdP15eXpb17Tm/uTIyMtSxY0fVrl1bH374oYKCgnTy5Elt3rxZFy9ezFOvLl266MKFC3rttdfUs2dP7dy5U1FRUZLsO+5PP/20pk6dapXm6+tr+ffixYs1bNgwPfHEE1q3bp1CQ0N1/PhxrVixQnPnztW8efMKPnh2uv4+XbNmjSZNmmR1Xn18fCRJb775pkaNGqVx48Zp4cKF8vLy0vr16zV06FD98MMPmjNnjho2bKiJEydqyJAhatWqlcqXL6/Tp09r6NCheuWVV1S/fn2bdbhw4YJat26tixcvavr06WratKk8PDy0fft2jR07Vu3bt1eZMmXyrDdlyhS9/fbb+sc//qHIyEilpqZq3759On/+vFW+Ll26WP4Dt2PHDg0ePFhpaWmKjY215CnoPpdufr0fO3ZMrVq1UpkyZTR79mzde++9yszM1JYtWzR8+HD99NNP+Z0CALirEecR5+UiziPOI84jziuJaBgEnCAuLk59+/bVAw88oOHDh2vChAny8vJSYGCgJY+Pj4/S09Ot0q5XqVIlm38QbNm0aZNat26tl156yZIWERGR58mqyWSybC8oKEiTJ09Wv3799Ouvv6pWrVqSrgUP+dUpV/ny5fPNM3/+fH355Zfat2+fGjdubEkPDw9Xz549lZGRYdc+3Y7Zs2dr0qRJWrlypXr06GG1zMPD46b7d6M+ffpo48aNeueddzRs2LB8811/fAvD9u3bdeXKFU2dOlUrVqzQl19+qTZt2qhixYqWPOXKlZOU//Vjz/nNdejQIR05ckRffPGFQkJCJEkhISFq1apVnrxlypRRYGCgAgMDtWjRIq1evVobNmywBIz2HHdfX99885w8eVIjR47UyJEj9frrr1vSQ0ND1aZNG4d7HhTk+joEBATYPK8nTpzQ6NGjNWrUKMXExFjSR48eLS8vL40cOVI9e/ZU8+bNNX78eG3YsEHDhw/X6tWr9cwzz6hmzZoaM2ZMvnWYMGGCjh07pp9//llVqlSxpEdERKhPnz759lDYuHGjhg0bpp49e1rSGjZsmCeft7e3ZZ/69u2rrVu36uOPP7YKGAu6z6WbX+/Dhg2TyWTSN998o1KlSlnS69Wrp6eeeirf9QDgbkecR5xXGIjziPOI83C3YCgxcJsuXbqkDz74QP369VPHjh2Vlpambdu2Feo2AwMD9eOPP1q6q9sr94lY7rAPZ3jvvffUoUMHq2Axl6enp9UfjsLw8ssva9q0adq0aVOeYPFW+fv7a8KECZo6daqlG35RiIuLU58+feTp6ak+ffooLi6uULdXsWJFubm5ae3atZYn1fbw9PSUh4eHU6+rDz74QBkZGRo7dqzN5fb+58pZ1q5dq8zMTJtB3zPPPKPSpUtr1apVkiR3d3ctX75c69evV9++fbVlyxbFx8dbem/cKCcnR6tXr9bjjz9uFSzmKl26dL69HwIDA/XFF1/ozJkzDu2Pj4+PU8/Xf/7zH3366acaPny4zXv+Tp8vAHAW4jzivMJCnEecJxHn4e5AwyCQj02bNql06dJWn65du+bJt3r1atWsWVP16tWTu7u7evfufUt/2KtWrWq1rdwnvbaMGDFCTZs2VYMGDRQaGqrevXtr6dKlSk9Pz3edkydP6rXXXlPVqlUVERFhSR83blye/bwx4G3ZsmWePLkBxS+//GI1p0hB7D2m9vrXv/6lWbNmaf369erQoYPNPN9//32ebV4/BCU/w4YNk9lsLnAYw8WLF/OU3alTp5uWfeO5Ll26tI4fP26VJzU1VevWrVO/fv0kSf369dPatWuVmpp60/KvZ8/5zXXPPfdowYIFmjRpksqWLav27dtr2rRpOnLkSL7lp6ena/r06UpNTdWDDz5oSbfnuC9cuDBPnuXLl0u6dl35+/srKCjIrv2055jejp9//lkBAQE26+Pl5aXw8HCr+Xnq1KmjUaNGadWqVZoyZYrVPXejs2fP6vz583bfR9ebN2+ezpw5o8DAQN17770aOnSo/vWvfxW4zjfffKOVK1danS+p4PtcKvh6//XXX2UYht37YOu6vP4JPQAUNuK8PxHn2UacR5wnEedJxHmugKHEQD7atWtn1f1akvbs2WP5A54rLi7OKq1fv36WLvCOPD3ZsWOH1ZwgBc2PUqpUKX3yySf67bfftHXrVu3evVujR4/WG2+8oa+//toyf0fuD7xhGLp8+bLuu+8+ffjhh1Zzk7z00ktWk+5K1wKH661Zs0Z16tSxSst9KmYYht3zrdh7TO1177336uzZs5o0aZKaNm1qc06VWrVq5ZkgOL+5V67n7e2tqVOn6rnnntOzzz5rM4+fn5++/fZbq7Tcp/UFufFcS7JMJJ1r5cqVCg8PtwwXaNSokcLDw7V69WoNGTLkptvIZc/5vd7w4cM1YMAAbd26VXv27NEHH3ygmJgYbdiwQR07drTk69Onj9zd3XXlyhUFBARozpw5VsG/Pcf98ccfV3R0tFVa7nxLjlxXkn3HtDDdWN8//vhDa9aska+vr3bs2JHvE/HcdaVbm7eobt2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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_plot, classification_report_2, metrics = plot_count_and_normalized_confusion_matrix(\n",
    "    y_true_2, y_pred_2, display_labels_2, labels_2, xticks_rotation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "20289ecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log metrics\n",
    "for metric_name, metric_value in metrics.items():\n",
    "    wandb.log({metric_name: metric_value})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "09a5abfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log the confusion matrix matplotlib figure\n",
    "wandb.log({'confusion_matrix_2': wandb.Image(cm_plot)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2399e29a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact classification_report_2_ds_5_t_1_trn_250>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Log the classification report as an artifact\n",
    "classification_report_2 = (pd.DataFrame({k: v for k, v in classification_report_2.items() if k != 'accuracy'})\n",
    "                         .transpose().reset_index())\n",
    "\n",
    "wandb.log({'classification_report_2': wandb.Table(\n",
    "    dataframe=classification_report_2)})\n",
    "\n",
    "classification_report_artifact_2 = wandb.Artifact(\n",
    "      f'classification_report_2_{model_name}', type='classification_report')\n",
    "\n",
    "with classification_report_artifact_2.new_file('classification_report_2.txt', mode='w') as f:\n",
    "    f.write(pprint.pformat(classification_report_2))\n",
    "\n",
    "wandb.run.log_artifact(classification_report_artifact_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b0aa2d8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "output_type": "display_data"
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     "data": {
      "text/html": [
       "<style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>█▁</td></tr><tr><td>f1</td><td>█▁</td></tr><tr><td>precision</td><td>█▁</td></tr><tr><td>recall</td><td>█▁</td></tr><tr><td>train_loss</td><td>▂▂▂▁▁▁▁▁▂▄▁▂▂▃▁█</td></tr><tr><td>train_mean_token_accuracy</td><td>▄▄▄█████▅▁█▄▅▄█▁</td></tr><tr><td>valid_loss</td><td>▁█▆▂▆▂▄▅▆▃▁▄▂▂▃▂</td></tr><tr><td>valid_mean_token_accuracy</td><td>█▁▂▆▁▇▆▆▅▆▇▆▆▆▃▆</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>0.65992</td></tr><tr><td>f1</td><td>0.65992</td></tr><tr><td>precision</td><td>0.65992</td></tr><tr><td>recall</td><td>0.65992</td></tr><tr><td>train_loss</td><td>0.10857</td></tr><tr><td>train_mean_token_accuracy</td><td>0.98571</td></tr><tr><td>valid_loss</td><td>0.04378</td></tr><tr><td>valid_mean_token_accuracy</td><td>0.98639</td></tr></table><br/></div></div>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run <strong style=\"color:#cdcd00\">finetune_chetGPT_hatespeech_prolific</strong> at: <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/l9oes8e6' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/l9oes8e6</a><br/>Synced 6 W&B file(s), 4 media file(s), 4 artifact file(s) and 0 other file(s)"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Find logs at: <code>.\\wandb\\run-20250228_140748-l9oes8e6\\logs</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
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   "source": [
    "# Mark the end of the run\n",
    "wandb.finish()"
   ]
  }
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  "kernelspec": {
   "display_name": "chatGPT",
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